2019 Publications

Scientific Publications

  • Self-supervised learning for single view depth and surface normal estimation

    Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. (2019). Self-supervised learning for single view depth and surface normal estimation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 4811–4817. https://doi.org/10.1109/ICRA.2019.8793984

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  • Attention-guided network for ghost-free high dynamic range imaging

    Yan, Q., Gong, D., Shi, Q., Van Den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided network for ghost-free high dynamic range imaging. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1751–1760. https://doi.org/10.1109/CVPR.2019.00185

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  • Bayesian Generative Active Deep Learning

    Tran, T., Do, T.-T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. Retrieved from https://arxiv.org/pdf/1904.11643

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  • Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

    Purkait, P., Zach, C., & Reid, I. (2019). Seeing behind Things: Extending Semantic Segmentation to Occluded Regions. IEEE International Conference on Intelligent Robots and Systems, 1998–2005. https://doi.org/10.1109/IROS40897.2019.8967582

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  • NeuRoRA: Neural Robust Rotation Averaging

    Purkait P., Chin TJ., Reid I. (2020) NeuRoRA: Neural Robust Rotation Averaging. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_9

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  • Below horizon aircraft detection using deep learning for vision-based sense and avoid

    James, J., Ford, J. J., & Molloy, T. L. (2019). Below horizon aircraft detection using deep learning for vision-based sense and avoid. 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019, 965–970. https://doi.org/10.1109/ICUAS.2019.8798096

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  • Reinforcement Learning with Attention that Works: A Self-Supervised Approach

    Manchin, A., Abbasnejad, E., & van den Hengel, A. (2019). Reinforcement Learning with Attention that Works: A Self-Supervised Approach. Communications in Computer and Information Science, 1143 CCIS, 223–230. https://doi.org/10.1007/978-3-030-36802-9_25

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  • BTEL: A Binary Tree Encoding Approach for Visual Localization

    Le, H., Hoang, T., & Milford, M. J. (2019). BTEL: A Binary Tree Encoding Approach for Visual Localization. IEEE Robotics and Automation Letters, 4(4), 4354–4361. https://doi.org/10.1109/LRA.2019.2932580

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  • Filter Early, Match Late: Improving Network-Based Visual Place Recognition

    Hausler, S., Jacobson, A., & Milford, M. (2019). Filter Early, Match Late: Improving Network-Based Visual Place Recognition. IEEE International Conference on Intelligent Robots and Systems, 3268–3275. https://doi.org/10.1109/IROS40897.2019.8967783

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  • Forecasting Future Action Sequences with Neural Memory Networks

    Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Forecasting Future Action Sequences with Neural Memory Networks. Retrieved from http://arxiv.org/abs/1909.09278

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  • Visual place recognition for aerial robotics: Exploring accuracy-computation trade-off for local image descriptors

    Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M., & McDonald-Maier, K. D. (2019). Visual place recognition for aerial robotics: Exploring accuracy-computation trade-off for local image descriptors. Proceedings - 2019 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2019, 103–108. https://doi.org/10.1109/AHS.2019.00011

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  • SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

    Abedin, A., Hamid Rezatofighi, S., Shi, Q., & Ranasinghe, D. C. (2019). Sparsesense: Human activity recognition from highly sparse sensor data-streams using set-based neural networks. IJCAI International Joint Conference on Artificial Intelligence, 2019-August, 5780–5786. https://doi.org/10.24963/ijcai.2019/801

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  • Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-tagged Objects

    Nguyen, H. Van, Rezatofighi, H., Vo, B. N., & Ranasinghe, D. C. (2019). Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects. IEEE Transactions on Signal Processing, 67(20), 5365–5379. https://doi.org/10.1109/TSP.2019.2939076

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  • Deep attention-based classification network for robust depth prediction

    Li R., Xian K., Shen C., Cao Z., Lu H., Hang L. (2019) Deep Attention-Based Classification Network for Robust Depth Prediction. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_41

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  • Mask-aware networks for crowd counting

    Jiang, S., Lu, X., Lei, Y., & Liu, L. (2020). Mask-Aware Networks for Crowd Counting. IEEE Transactions on Circuits and Systems for Video Technology, 30(9), 3119–3129. https://doi.org/10.1109/TCSVT.2019.2934989

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  • Sim-to-real transfer of robot learning with variable length inputs

    Dasagi, V., Lee, R., Mou, S., Bruce, J., Sünderhauf, N., & Leitner, J. (2018). Sim-to-Real Transfer of Robot Learning with Variable Length Inputs. Australasian Conference on Robotics and Automation, ACRA, 2019-December. Retrieved from http://arxiv.org/abs/1809.07480

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  • From Selective Deep Convolutional Features to Compact Binary Representations for Image Retrieval

    Do, T. T., Hoang, T., Le Tan, D. K., Le, H., Nguyen, T. V., & Cheung, N. M. (2019). From selective deep convolutional features to compact binary representations for image retrieval. ACM Transactions on Multimedia Computing, Communications and Applications, 15(2), 1–22. https://doi.org/10.1145/3314051

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  • Attitude Observation for Second Order Attitude Kinematics

    Ng, Y., Van Goor, P., Mahony, R., & Hamel, T. (2019). Attitude Observation for Second Order Attitude Kinematics. Proceedings of the IEEE Conference on Decision and Control, 2019-December, 2536–2542. https://doi.org/10.1109/CDC40024.2019.9029785

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  • A novel passivity-based trajectory tracking control for conservative mechanical systems

    Mahony, R. (2019). A novel passivity-based trajectory tracking control for conservative mechanical systems. Proceedings of the IEEE Conference on Decision and Control, 2019-December, 4259–4266. https://doi.org/10.1109/CDC40024.2019.9029222

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  • Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy

    Camps, S.M., Houben, T., Carneiro, G., Edwards, C., Antico, M., Dunnhofer, M., Martens, E.G.H.J., Baeza, J.A., Vanneste, B.G.L., van Limbergen, E.J., de With, P.H.N., Verhaegen, F., & Fontanarosa, D. (2019) Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy. In ASMIRT / AACRT 2019 Conference, 28-31 March 2019, Adelaide, S.A

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  • Spectral-GANs for High-Resolution 3D Point-cloud Generation

    Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Spectral-GANs for High-Resolution 3D Point-cloud Generation. Retrieved from http://arxiv.org/abs/1912.01800

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  • Adversarial discriminative sim-to-real transfer of visuo-motor policies

    Zhang, F., Leitner, J., Ge, Z., Milford, M., & Corke, P. (2019). Adversarial discriminative sim-to-real transfer of visuo-motor policies. The International Journal of Robotics Research, 38(10–11), 1229–1245. https://doi.org/10.1177/0278364919870227

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  • Multi-Modal Generative Models for Learning Epistemic Active Sensing

    Korthals, T., Rudolph, D., Leitner, J., Hesse, M., & Ruckert, U. (2019). Multi-modal generative models for learning epistemic active sensing. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 3319–3325. https://doi.org/10.1109/ICRA.2019.8794458

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  • High‐throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare)

    Ward, B., Brien, C., Oakey, H., Pearson, A., Negrão, S., Schilling, R. K., … van den Hengel, A. (2019). High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). Plant Journal, 98(3), 555–570. https://doi.org/10.1111/tpj.14225

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  • What’s to Know? Uncertainty as a Guide to Asking Goal-Oriented Questions

    Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2019). What’s to know? uncertainty as a guide to asking goal-oriented questions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4150–4159. https://doi.org/10.1109/CVPR.2019.00428

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  • Accurate Tensor Completion via Adaptive Low-Rank Representation

    Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate Tensor Completion via Adaptive Low-Rank Representation. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/tnnls.2019.2952427

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  • Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification

    Zhang, X., Cao, J., Shen, C., & You, M. (2019). Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification *.

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  • Unsupervised object discovery and co-localization by deep descriptor transformation

    Wei, X. S., Zhang, C. L., Wu, J., Shen, C., & Zhou, Z. H. (2019). Unsupervised object discovery and co-localization by deep descriptor transformation. Pattern Recognition, 88, 113–126. https://doi.org/10.1016/j.patcog.2018.10.022

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  • Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval

    Zhao, Y., Shen, C., Wang, H., & Chen, S. (2020). Structural Analysis of Attributes for Vehicle Re-Identification and Retrieval. IEEE Transactions on Intelligent Transportation Systems, 21(2), 723–734. https://doi.org/10.1109/TITS.2019.2896273

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  • Real-time Tracker with Fast Recovery from Target Loss

    Bay, A., Sidiropoulos, P., Vazquez, E., & Sasdelli, M. (2019). Real-time Tracker with Fast Recovery from Target Loss. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 1932–1936. https://doi.org/10.1109/ICASSP.2019.8682171

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  • VizieR Online Data Catalog: TESS planet candidates classification (Osborn+, 2020)

    Osborn, H. P., Ansdell, M., Ioannou, Y., Sasdelli, M., Angerhausen, D., Caldwell, D. A., Jenkins, J. M., Raissi, C., & Smith, J. C. (2019). VizieR Online Data Catalog: TESS planet candidates classification (Osborn+, 2020). YCat, J/A+A/633/A53.

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  • Depth Based Semantic Scene Completion With Position Importance Aware Loss

    Li, J., Liu, Y., Yuan, X., Zhao, C., Siegwart, R., Reid, I., & Cadena, C. (2020). Depth Based Semantic Scene Completion with Position Importance Aware Loss. IEEE Robotics and Automation Letters, 5(1), 219–226. https://doi.org/10.1109/LRA.2019.2953639

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  • Measurement of the average very forward energy as a function of the track multiplicity at central pseudorapidities in proton-proton collisions at s√=13TeV

    Sirunyan, A.M., Tumasyan, A., Adam, W., Ambrogi, F., Bergauer, T., Brandstetter, J., Dragicevic, M., Erö, J., Escalante Del Valle, A., Flechl, M., Frühwirth, R., Jeitler, M., Krammer, N., Krätschmer, I., Liko, D., Madlener, T., Mikulec, I., Rad, N., Schieck, J., Schöfbeck, R., Spanring, M., Spitzbart, D., Waltenberger, W., Wittmann, J., Wulz, C.-E., Zarucki, M., Drugakov, V., Mossolov, V., Suarez Gonzalez, J., Darwish, M. R., De Wolf, E. A., Di Croce, D., Janssen, X., Lauwers, J., Lelek, A., Pieters, M., Van Haevermaet, H., Van Mechelen, P., Van Remortel, N., Blekman, F., Chhibra, S. S., D'Hondt, J., De Clercq, J., Glouris, G., Lontkovskyi, D., Lowette, S., Marchesini, I., Moortgat, S., Moreels, L., Python, Q., Skovpen, K., Tavernier, S., Van Doninck, W., Van Mulders, P., Van Parijs, I., Beghin, D., Bilin, B., Brun, H., Clerbaux, B., De Lentdecker, G., Delannoy, H., Dorney, B., Favart, L., Grebenyuk A., Kalsi, A. K., Luetic, J., Popov, A., Postiau, N., Starling, E., Thomas, L., Vander Velde, C., Vanlaer, P., Vannerom, D., Wang, Q., Cornelis, T., Dobur, D., Fagot, A., Gul, M., Khvastunov, I., Roskas, C., Trocino, D., Tytgat, M., Verbeke, W., Vermassen, B., Vit, MZaganidis, N., Bondu, O., Bruno, G., Caputo, C., David, P., Delaere, C., Delcourt, M., Giammanco, A., Vischia, P., Zobec, J., Alves, F. L., Alves, G. A., Correia Silva, G., Hansel, C., Moraes, A., Rebello Teles, P., Belchior Batista Das Chagas, E., Carvalho, W., Chinellato, J., Coelho, E., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., De Oliveira Martins, C., Fonseca De Souza, S., Huertas Guativa, L. M., Malbouisson, H., Matos Figueiredo, D., Medina Jaime, M., Melo De Almeida, M., Mora Herrera, C., Mundim, L., Nogima, H., Prado Da Silva, W. L., Sanchez Rosas, L. J., Santoro, A., Sznajder, A., Thiel, M., Tonelli Manganote, E. J., Torres Da Silva De Araujo, F., Vilela Pereira, A., Ahuja, S., Bernardes, C. A., Calligaris, L., De Souza Lemos, D., Fernandez Perez Tomei, T. R., Gregores, E. M., Mercadante, P. G., Novaes, S. F., Padula, S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Laydjiev, P., Marinov, A., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Fang, W., Gao, X., Yuan, L., Ahmad, M., Chen, G. M., Chen, H. S., Chen, M., Jiang, C. H., Leggat, D., Liao, H., Liu, Z., Shaheen, S. M., Spiezia, A., Tao, J., Yazgan, E., Zhang, H., Zhang, S., Zhao, J., Agapitos, A., Ban, Y., Chen, G., Levin, A., Li, J., Li, L., Li, Q., Mao, Y., Qian, S. J., Wang, D., Wang, Y., Avila, C., Cabrera, A., Chaparro Sierra, L. F., Florez, C., González Hernández, C. F., Segura Delgado, M. A., Ruiz Alvarez, J. D., Giljanović,D., Godinovic, N., Lelas, D., Puljak, I., Sculac, T., Antunovic, Z., Kovac, M., Brigljevic, V., Ferencek, D., Kadija, K., Mesic, B., Roguljic, M., Starodumov, A., Susa, T., Ather, M. W., Attikis, A., Erodotou, E., Ioannou, A., Kolosova, M., Konstantinou, S., Mavromanolakis, G., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Tsiakkouris, D., Finger, M., Finger Jr, M., Ayala, E., Carrera Jarrin, E., Mahmoud, M. A., Mahammad, Y., Bhowmik, S., Carvalho Antunes De Oliveira, A., Dewanjee, R. K., Ehataht, K., Kadastik, M., Raidal, M., Veelken, C., Eerola, P., Kirschenmann, H., Osterberg, K., Pekkanen, J., Voutilainen, M., Garcia, F., Havukainen, J., Heikkilä, J. K., Järvinen, J., Karimäki, V., Kinnunen, R., Lampén, T., Lassila-Perini, K., Laurila, S., Lehti, S., Lindén, T., Luukka, P., Mäenpää, T., Siikonen, H., Tuominen, E., Tuominiemi, J., Tuuva, T., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Fabbro, B., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., Hamel de Monchenault, G., Jarry, P., Leloup, C., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Titov, M., Amendola, C., Beaudette, F., Busson, P., Charlot, C., Diab, B., Granier de Cassagnac, R., Kucher, I., Lobanov, A., Martin Perez, C., Nguyen, M., Ochando, C., Paganini, P., Rembser, J., Salerno, R., Sauvan, J. B., Sirois, Y., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Bloch, D., Bourgatte, G., Brom, J.-M., Chabert, E. C., Collard, C., Conte, E., Fontaine, J.-C., Gelé, D., Goerlach, U., Jansová,M., Le Bihan, A.-C., Tonon, N. Van Hove, P., Gadrat, S., Beauceron, S., Bernet, C., Boudoul, G., Camen, C., Chanon, N., Chierici, R., Contardo, D., Depasse, P., El Mamouni, H., Fay, J., Gascon, S., Gouzavitch, M., Ille, B., Jain, Sa., Lagarde, F., Laktineh, I. B., Lattaud, H., Lethuillier, M., Mirabito, L., Perriees, S., Sordini, V., Touquet, G., Vander Donckt, M., Viret, S., Khvedelidze, A., Tsamalaidze, Z., Autermann, C., Feld, L., Kiesel, M. K., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Preuten, M., Rauch, M. P., Schomakers, C., Schulz, J., Teroerde, M., Wittmer, B., Albert, A., Erdmann, M., Erdweg, S., Esch, T., Fischer, B., Fischer, R., Shosh, S., Hebbeker, T., Hoepfner, K., Keller, H., Mastrolorenzo, L., Merschmeyer, M., Meyer, A., Millet, P., Mocellin, G., Mondal, S., Mukherjee, S., Noll, D., Novak, A., Pook, T., Pozdnyakov, A., Quast, T., Radziej, M., Rath, Y., Reithler, H., Rieger, M., Schmidt, A., Schuler, S. C., Sharma, A., Thüer, S., Wiedenbeck, S., Flügge, G., Hlushchenko, O, Kress, T., Müller, T., Nehrkorn, A., Nowack, A., Pistone, C., Pooth, O., Roy, D., Sert, H., Stahl, A., Aldaya Martin, M., Asawatangatrakuldee, C., Asmuss, P., Babounikau, I., Bakhshiansohi, H., Beernaert, K., Behnke, O., Behrens, U., Bermúdez Martínez, A., Bertsche, D., Bin Anuar, A. A., Borras, K., Botta, V., Campbell, A., Cardini, A., Connor, P., Consuegra Rodríguez, S., Contreras-Campana, C., Danilov, V., De Wit, A., Defranchis, M. M., Diez Pardos, C., Domínguez Damiani, D., Eckerlin, G., Eckstein, D., Eichhorn, T., Elwood, A., Eren, E., Gallo, E., Geiser, A., Grados Luyando, J. M., Grohsjean, A., Guthoff, M., Haranko, M., Harb, A., Jomhari, N. Z., Jung, H., Kasem, A., Kasemann, M., Keaveney, J., Kleinwort, C., Knolle, J., Krücker, D., Lange, W., Lenz, T., Leonard, J., Lidrych, J., Lipka, K., Lohmann, W., Mankel, R., Melzer-Pellmann, I.-A., Meyer, A. B., Meyer, M., Missiroli, M., Mittag, G., Mnich, G., Mcich, J., Mussgiller, A., Myronenko, V., Pérez Adán, D., Pflitsch, S. K., Pitzl, D., Raspereza, A., Saibel, A., Savitskyi, M., Scheurer, V., Schütze, P., Schwanenberger, C., Shevchenko, R., Singh, A., Tholen, H., Turkot, O., Vagnerini, A., Van De Klundert, M., Van Onsem, G. P., Walsh, R., Wen, Y., Wichmann, K., Wissing, C., Zenaiev, O., Zlebcik, R., Aggleton, R., Bein, S., Benato, L., Benecke, A., Blobel, V., Dreyer, T., Ebrahimi, A., Fröhlich, A., Garbers, C., Garutti, E., Gonzalez, D., Gunnellini, P., Haller, J., Hinzmann, A., Karavdina, A., Kasieczka, G., Klanner, R., Kogler, R., Kovalchuk, N., Kurz, S., Kutzner, V., Lange, J., Lange, T., Malara, A., Marconi, D., Multhaup, J., Niedziela, M., Niemeyer, C. E. N., Nowatschin, D., Perieanu, A., Reimers, A., Rieger, O., Scharf, C., Schleper, P., Schumann, S., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrück, G., Stober, F. M., Stöver, M., Cormwald, B., Zoi, I., Akbiyik, M., Barth, C., Baselga, M., Baur, S., Berger, T., Butz, E., Caspart, R., Chwalek, T., De Boer, W., Dierlamm, A., El Morabit, K., Faltermann, N., Giffels, M., Goldenzweig, P., Harrendorf, M. A., Hartmann, F., Husemann, U., Kudella, S., Mitra, S., Mozer, M. U., Müller, Th., Musich, M., Nürnberg, A., Quast, G., Rabbertz, K., Schröder, M., Shvetsov, I., Simonis, H. J., Ulrich, R., Weber, M., Wöhrmann, C., Wolf, R., Anagnostou, G., Asenov, P., Daskalakis, G., Geralis, T., Kyriakis, A., Loukas, D., Paspalaki, G., Diamantopoulou, M., Karathanasis, G., Kontaxakis, P., Panagiotou, A., Papavergou, I., Saoulidou, N., Theofilatos, K., Vellidis, K., Bakas, G., Kousouris, K., Papakrivopoulos, I., Tsipolitis, G., Evangelou, I., Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Mallios, S., Manitara, K., Manthos, N., Papadopoulos, I., Paradas, E., Strologas, J., Triantis, F. A., Tsitsonis, D., Bartók, M., Csanad, M., Major, P., Mandel, K., Mehta, A., Nagy, M. I., Pasztor, G., Surányi, O., Veres, G. I., Bencze, G., Hajdu, C., Horvath, D., Hunyadi, Ã., Sikler, F., Vámi, T. Ã., Veszpremi, V., Vesztergombi, G., Beni, N., Czellar, S., Karancsi, J., Makovec, A., Molnar, J., Szillasi, Z., Raics, P., Teyssier, D., Trocsanyi, Z. L., Ujvari, B., Csorgo, T. F., Nemes, F., Novak, T., Choudhury, S., Komaragiri, J. R., Tiwari, P. C., Bahinipati, S., Kar, C., Kole, G., Mal, P., Muraleedharan Nair Bindu, V. K., Nayak, A., Roy Chowdhury, S., Sahoo, D. K., Swain, S. K., Bansal, S., Beri, S. B., Bhatnagar, V., Chauhan, S., Chawla, R., Dhingra, N., Gupta, R., Kaur, A., Kaur, M., Karu, S., Kumari, P., Lohan, M., Meena, M., Sandeep, K., Sharma, S., Singh, J. B., Virdi, A. K., Walia, G., Bhardwaj, A., Choudhary, B. C., Garg, R. B., Gola, M., Keshri, S., Kumar, A., Malhotra, S., Naimuddin, M., Priyanka, P., Ranjan, K., Shah, A., Sharma, R., Bhardwaj, R., Bharti, M., Bhattacharya, R., Bhattacharya, S., Bhawandeep, U., Bhowmik, D., Dey, S., Dutta, S., Ghosh, S., Maity, M., Mondal, K., Nandan, S., Purohit, A., Rout, P. K., Rou, A., Saha, G., Sarkar, S., Sarkar, T., Sharan, M., Singh, B., Thakur, S., Behera, P. K., Muhammad, A., Chudasama, R., Dutta, D., Jha, V., Kumar, V., Mishra, D. K., Netrakanti, P. K., Pant, L. M., Shukla, P., Aziz, T., Bhat, M. A., Dugad, S., Mohanty, G. B., Sur, N., Kumar Verman, R., Banerjee, S., Bhattacharya, S., Chatterjee, S., Das, P., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Sahoo, N., Sawant, S., Chauhan. S., Dube, S., Hegde, V., Kapoor, A., Kothekar, K., Pandey, S., Rane, A., Rastogi, A., Sharma, S., Chenarani, S., Eskandari Tadavani, E., Etesami, S. M., Khakzad, M., Mohammadi Najafabadi, M., Naseri, M., Rezaei Hosseinabadi, F., Safarzadeh, B., Fecini, M., Grunewald, M., Abbrescia, M., Calabria, C., Colaleo, A., Creanza, D., Cristella, L., De Filippis, N., De Palma, M., Di Florio., Fiore, L., Gelmi, A., Iaselli, G., Ince, M., Lezki, S., Maggi, G., Maggi, M., Miniella, G., My, S., Nuzzo, S., Pompili, A., Pugliese, G., Radogna, R., Ranieri, A., Selvaggi, G., Silvestris, L., Venditti, R., Verqilligen, P., Abbiendi, G., Battilana, C., Bonacorsi, D., Borgonovi, L., Braibant-Giacomelli, S., Campanini, R., Capiluppi, P., Castro, A., Cavallo, F. R., Ciocca, C., Codispoti, G., Cuffiani, M., Dallavalle, G. M., Gabbri, F., Fanfani, A., Fontanesi, E., Giacomelli, P., Grandi, C., Guiducci, L., Iemmi, F., Lo Meo, S., Marcellini, S., Masetti, G., Navarria, F. L., Perrotta, A., Primavera, F., Rossi, A. M., Rovelli, T., Siroli, G. P., Tosi, N., Albergo, S., Costa, S., Di Mattia, A., Potenza, R., Tricomi, A., Tuve, C., Barbagli, G., Ceccarelli, R., Chatterjee, K., Ciulli, V., Civinini, C., D'Alessandra, R., Focardi, E., Latina, G., Lenzi, P., Meschini, M., Paoletti, S., Russo, L., Sguazzoni, G., Strom, D., Viliani, L., Benussi, L., Bianco, S., Fabbri, F., Piccolo, D., Bozzo, M., Ferro, F., Mulargia, R., Robutti, E., Tosi, S., Benaglia, A., Beschi, A., Brivio, F., Ciriolo, V., Di Guida, S., Dinardo, M. E., Dini, P., Fiorendi, S., Gennai, S., Ghezzi, A., Govoni, P., Malberti, M., Malvezzi, S., Menasce, D., Monti, F., Moroni, L., Ortona, G., Paganoni, M., Pedrini, D., Ragazzi, S., Tabarelli de Fatis, T., Zuolo, D., Buontempo, S., Cavallo, N., De Lorio, A., Di Crescenzo, A., Fabozzi, F., Fienga, F., Galati, G., Iorio, A. O. M., Lista, L., Meola, S., Paolucci, P., Rossi, B., Sciacca, C., Voevodina, E., Azzi, P., Bacchetta, N., Bisella, D., Boletti, A., Bragagnolo, A., Carlin, R., Checchia, P., Dall'Osso, M., De Castro Manzano, P., Dorigo, T., Dosselli, U., Gasparini, F., Gasparini, U., Gozzelino, A., Hoh, S. Y., Lujan, P., Margoni, M., Meneguzzo, A. T., Pazzini, J., Presilla, M., Ronchese, P., Rossin, R., Simonetto, F., TIko, A., Torassa, E., Tosi, M., Zanetti, M., Zotto, P., Zumerle, G., Braghieri, A., Montagna, P., Ratti, S. P., Re, V., Ressegotti, M., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Biasini, M., Bilei, G. M., Cecchi, C., Ciangottini, D., Fanò, L., Lariccia, P., Leonardi, R., Manoni, E., Mantovani, G., Mariani, V., Menichelli, M., Rossi, A., Santocchia, A., Spiga, D., Androsov, K., Azzurri, P., Bagliesi, G., Bertacchi, V., Bianchini, L., Boccali, T., Castaldi, R., Ciocci, M. A., Dell'Orso, R., Fedi, G., Fiori, F., Giannini, L., Giassi, A., Grippo, M. T., Ligabue, F., Manca, E., Mandorli, G., Messineo, A., Palla, F., Rizzi, A., Rolandi, G., Scribano, A., Spagnolo, P., Tenchini, R., Tonelli, G., Turini, N., Venturi, A., Verdini, P. G., Cavallari, F., Cipriani, M., Del Re, D., Di Marco, E., Diemoz, M., Gelli, S., Longo, E., Marzocchi, B., Meridiani, P., Organtini, G., Pandolfi, F., Paramatti, R., Preiato, F., Quaranta, C., Rahatlou, S., Rovelli, C., Santanastasio, F., Soffi, L., Amapane, N., Arcidiacono, R., Argiro, S., Arneodo, M., Bartosik, N., Bellan, R., Biino, C., Cappati, A., Cartiglia, N., Cartiglia, N., Cenna, F., Cometti, S., Costa, M., Covarelli, R., Demaria, N., Kiani, B., Mariotti, C., Maselli, S., Migliore, E., Monaco, V., Montiel, E., Monteno, M., Obertino, M. M., Pacher, L., Pastronie, N., Pelliccioni, M., Pinna Angioni, G. L., Romero, A., Ruspa, M., Sacchi, R., Salvatico, R., Shchelina, K., Sola, V., Solano, A., Soldi, D., Staiano, A., Belforte, S., Candelise, V., Casarsa, M., Cossutti, F., Da Rold, A., Della Ricca, G., Vazzoler, F., Zanetti, A., Kim, B., Kim, D. H., Kim, G. N., Kim, M. S., Lee, J., Lee, S. W., Moon, C. S., Oh, Y. D., Pak, S. I., Sekmen, S., Son, D. C., Yang, Y. C., Kim, H., Moon, D. H., Oh, G., Francois, B., Kim, T. J., Park, J., Cho, S., Choi, S., Go, Y., Gyun, D., Ha, S., Hong, B., Jo, Y., Lee, K., Lee, K. S., Lee, S., Lim, J., Park, J., Park, S. K., Roh, Y., Goh, J., Kim, H. S., Almond, J., Bhyun, J. H., Choi, J., Jeon, S., Kim, J., Kim, J. S., Lee, H., Lee, K., Lee, S., Nam, K., Oh, S. B., Radburn-Smith, B. C., Seo, S. H., Yang, U. K., Yoo, H. D., Yoon, I., Yu, G. B., Jeon, D., Kim, H., Kim, J. H., Lee, J. S. H., Park, I. C., Choi, Y., Hwang, C., Jeong, Y., Lee, J., Lee, Y., Yu, I., Veckalns, V., Dudenas, V., Juodagalvis, A., Vaitkus, J., Ibrahim Z. A., Mohamad Idris, F., Wan Abdullah, W. A. T., Yusli, M. N., Zolkapli, Z., Benitez, J. F., Castaneda, Hernandez, A., Murillo Quijada, J. A., Valencia Palomo, L., Castilla-Valdez, H., De La Cruz-Burelo, E., Duran-Osuna, M. C., Heredia-De La Crus, I., Lopez-Fernandez, R., Rabadan-Trejo, R. I., Ramirez-Sanchez, G., Reyes-Almanza, R., Sanchez-Hernandez, A., Carrillo Moreno, C., Ramirez-Garcia, M., Vazquez Valencia, F., Eysermans, J., Pedraza, I., Salazar Ibarguen, H. A., Uribe Estrada, C., Morelos Pineda, A., Raicevic, N., Krofcheck, D., Bheesette, S., Butler, P. H., Ahmad, A., Ahmad, M., Hassan, Q., Hoorani, H. R., Khan, W. A., Shah, M. A., Shoaib, M., Wagas, M., Avati, V., Grzanka, L., Malawski, M., Bialkowska, H., Bluj, M., Boimska, B., Górski, M., Kazana, M., Szleper, M., Zalewski, P., Bunkowski, K., Byszuk, A., Doroba, K., Kalinowski, A., Konecki, M., Krolikowski, J., Misiura, M., Olszewski, M., Pyskir, A., Walczak, M., Araujo, M., Bargassa, P., Bastos, D., Di Francesco, A., Faccioli, P., Galinhas, B., Gallinaro, M., Hollar, J., Leonardo, N., Seizas, J., Strong, G., Toldaiev, O., Varela, J., Alexakhin, V., Bunin, P., Ershov, Y., Gavrilenko, M., Golutvin, I., Kamenev, A., Karjavine, V., Kashunin, I., Korenkov, V., Kozlov, G., Lanev, A., Malakhov, A., Matveev, V., Moisenz, P., Palichik, V., Perelygin, V., Shmatov, S., Shulha, S., Yuldashev, B. S., Zarubin, A., Chtchipounov, L., Golovtsov, V., Ivanov, Y., Kim, V,., Kuznetsova, E., Levchenko, P., Murzin, V., Oreshkin, V., Smirnov, I., Sosnov, D., Sulimov, V., Uvarov, L., Vorobyev, A., Andreev, Yu., Dermenev, A., Gninenko, S., Golubev, N., Karneyeu, A., Kirsanov, M., Krasnikov, N., Pashenkov, A., Tlisov, D., Toropin, A., Epshteyn, V., Gavrilov, V., Lychkovskaya, N., Nikitenko, A., Popov, V., Pozdnyakov, I., Safronov, G., Spiridonov, A., Stepannov, A., Stolin, V., Toms, M., Vlasov, E,., Zhokin, A., Aushev, T., Chadeeva, M., Philippov, D., Popova, E., Rusinov, V., Andreev, V., Azarkin, M., Dremin, I., Kirakosyan, M., Terkulov, A., Belyaev, A., Boos, E., Ershov, A., Gribushin, A., Khein, L., Klyukhin, V., Kodolova, O., Lokhtin, I., Lukina, O., Obraztsov, S., Petrushanko, S., Savrin, V., Snigirev, A., Barnyakov, A., Blinov, V., Dimova, T., Kardapoltsev, L., Skovpen, Y., Azhgirey, I., Bayshev, I., Bitioukov, S., Kachanov, V., Konstantinov, D., Mandrik, P., Petrov, V., Ryutin, R., Slabospitskii, S., Sobol, A., Troshin, S., Tyurin, N., Uzunian, A., Volkov, A., Babaev, A., Iuzhakov, A., Okhotnikov, V., Borchsh, V., Ivantchenko, V., Tcherniaev, E., Adzic, P., Cirkovic, P., Devetak, D., Dordevic, M., Milenovic, P., Milosevic, J., Stojanovic, M., Aguilar-Benitez, M., Alcaraz Maestre, J., Ãlvarez Fernández, A., Bachiller, I., Barrio Luna, M., Brochero Cifuentes, J. A., Carrillo Montoya, C. A., Cepeda, M., Cerrada, M., Colino, N., De La Cruz, B., Delgado Peris, A., Fernandez Bedoya, C., Fernández Ramos, J. P., Flix, J., Frouz, M. C., Gonzalez Lopez, O., Goy Lopez, S., Hernandez, J. M., Josa, M. I., Moran, D., Navarro Tobar, Ã., Pérez-Calero Yzquierdo, A., Puerta Pelayo, J., Redondo, I., Romero, L., Sánchez Navas, S., Soares, M. S., Triossi, A., Willmott, C., Albajar, C., de Trocóniz, J. F., Cuevas, J., Erice, C., Fernandez Menendez, J., Folgueras, S., Gonzalez Caballero, I., González Fernández, J. R., Palencia Cortezon, E., Rodríguez Bouza, V., Sanchez Cruz, S., Vizan Garcia, J. M., Cabrillo, I. J., Calderon, A., Chazin Quero, B., Duarte Campderros, J., Fernandez, M., Fernández Manteca, P. J., García Alonso, A., Gomez, G., Martinez Rivero, C., Martinez Ruiz del Arbol, P., Matorras, F., Piedra Gomez, J., Prieels, C., Rodrigo, T., Ruiz-Jimeno, A., Scodellaro, L., Trevisani, N., Vila, I., Malagalage, K., Dharmaratna, W. G. D., Wickramage, N., Abbaneo, D., Akgun, B., Auffray, E., Auzinger, G., Baechler, J., Baillon, P., Ball, A. H., Barney, D., Bendavid, J., Bianco, M., Bocci, A., Bossini, E., Botta, C., Brondolin, E., Caporesi, T., Caratelli, A., Cerminara, G., Chapon, E., Cucciati, G., d'Enterria, D., Dabrowski, A., Daci, N., Daponte, V., David, A., De Roeck, A., Deelen, A., Deelen, N., Deile, M., Dobson, M., Dünser, M., Dunpont, N., Elliott-Peisert, A., Fallavollita, F., Fasanella, D., Franzoni, G., Fulcher, J., Funk, W., Giani, S., Gigi, D., Gilbert, A., Gill, K., Glege, F., Gruchala, M., Guilbaud, M., Gulhan, D., Hegeman, J., Heidegger, C., Iiyana, Y., Innocente, V., Jarafi, A., Janot, P., Karacheban, O., Kaspar, J., Kieseler, J., Krammer, M., Lange, C., Lecoq, P., Lourenco, C., Melgeri, L., Mannelli, M., Massironi, A., Meijers, F., Merlin, J. A., Mersi, S., Meschi, E., Moortgat, F., Mulders, M., Ngadiuba, J., Nourbakhsh, S., Orfanelli, S., Orsini, L., Pantaleo, F., Pape, L., Perez, E., Peruzzi, M., Petrilli, A., Petrucciani, G., Pfeiffer, A., Pierini, M., Pitters, F. M., Quinto, M., Rabady, D., Racz, A., Rovere, M., Sakulin, H., Schäfer, C., Schwick, C., Selvaggi, M., Sharma, A., Silva, P., Snoeys, W., Sphicas, P., Stakia, A., Steggemann, J., Tavolaro, V. R., Treille, D., Tsirou, A., Vartak, A., Verzetti, M., Zeuner, W. D., Caminada, L., Deiters, K., Erdmann, W., Horisberger, R., Ingram, Q., Kaestli, H. C., Kotlinski, D., Langenegger, U., Rohe, T., Wiederkehr, S. A., Backhaus, M., Berger, P., Chernyavskaya, N., Dissertori, G., Dittmar, M., Donegà, M., Dorfer, C., Gómez Espinosa, T. A., Grab, C., Hits, C., Klijnsma, T., Lustermann, W., Manzoni, R. A., Marionneau, M., Meinhard, M. T., Micheli, F., Musella, P., Nessi-Tedaldi, F., Pauss, F., Perrin, G., Perrozzi, L., Pigazzini, S., Reichmann, M., Reissel, C., Reitenspiess, T., Ruini, D., Sanz Becerra, D. A., Schönenberger, M., Shchutska, L., Vesterbacka Olsson, M. L., Wally, R., Zhu, D. H., Aarrestad, T. K., Amsler, C., Brzhechko, D., Canelli, M. F., De Cosa, A., Del Burgo, R., Donato, S., Galloni, C., Kilminster, B., Leontsinis, S., Mikuni, V. M., Neutelings, I., Rauco, G., Robmann P., Salerno, D., Schweiger, K., Seitz, C., Takahashi, Y., Wertz, S., Zucchetta, A., Doan, T. H., Kuo, C. M., Lin, W., Yu, S. S., Chang, P., Chao, Y., Chen, K. F., Chen, P. H., Hou, W.-S., Li, Y. Y., Lu, R.-S., Paganis, E., Psallidas, A., Steen, A., Asavapibhop, B., Srimanobhas, N., Suwonjandee, N., Bat, A., Boran, F., Cerci, S., Damarseckin, S., Demiroglu, Z. S., Dolek, F., Dozen, C., Dumanoglu, I., Gokbulut, G., Guler, E. G., Fuler, Y., Hos, I., Isik, C., Kangal, E. E., Kara, O., Kayis Topaksu, A., Kiminsu, U., Oglakci, M., Onengut, G., Ozdemir, K., Ozturk, S., Simsek, A. E., Sunar Cerci, D., Tok, U. G., Turkcapar, S., Zorbakir, I. S., Zorbilmez, C., Isildak, B., Karapinar, G., Yalvac, M., Ataksis, I. O., Gülmez, E., Kaya, M., Kaya, O., Kaynak, B., Özçelik, Ö., Ozkorucuklu, S., Tekten, S., Tekten, S., Yetkin, E. A., Cakir, A., Komurcu, Y., Sen, S., Grynyov, B., Levchuk, L., Ball, F., Bhal, E., Bologna, S., Brooke, J. J., Burns, D., Clement, E., Cussans, D., Davignon, O., Flacher, H., Goldstein, J., Heath, G. P., Heath, H. F., Kreczko, L., Paramesvaran, S., Penning, B., Sakuma, T., Seif El Nasr-Storey, S., Smith, D., Smith, V. J., Taylor, J., Titterton, A., Bell, K. W., Belyaev, A., Brew, C., Brown, R. M., Cieri, D., Cockerill, D. J. A., Coughlan, J. A., Harder, K., Harper, S., Linacre, J., Manolopoulos, K., Newbold, D. M., Olaiya, E., Petyt, D., Reis, T., Schuh, T., Shepherd-Themistocleous, C. H., Thea, A., Tomalin, I. R., Williams, T., Womersley, W. J., Bainbridge, R., Bloch, P., Borg, J., Breeze, S., Buchmuller, O., Bundock, A., Singh Chahal, G., Colling, D., Dauncey, P., Davies, G., Della Negra, M., Di Maria, R., Everaerts, P., Hall, G., Iles, G., James, T., Komm, M., Laner, C., Lyons, L., Magnan, A.-M., Malik, S., Martelli, A., Milosevic, V., Nash, J., Palladino, V., Pesaresi, M., Raymond, D. M., Richards, A., Rose, A., Scott, E., Seez, C., Shtipliyski, A., Stoye, M., Strebler, T., Summers, S., Tapper, A., Uchida, K., Virdee, T., Wardle, N., Winterbottom, D., Wright, J., Zecchinelli, A. G., Zenz, S. C., Cole, J. E., Hobson, P. R., Khan, A., Kyberd, P., Mackay, C. K., Morton, A., Reid, I. D., Teodorescu, L., Zahid, S., Call, K., Dittmann, J., Hatakeyama, K., Madrid, C., Mcmaster, B., Pastika, N., Smith, C., Bartek, R., Dominguez, A., Uniyal, R., Buccilli, A., Cooper, S. I., Henderson, C., Rumerio, P., West, C., Arcaro, D., Bose, T., Demiragli, Z., Gastler, D., Girgis, S., Pinna, D., Richardson, C., Rohlf, J., Sperka, D., Suarez, I., Sulak, L, Zou, D., Benelli, G., Burkle, B., Coubez, X., Cutts, D., Hadley, M., Hakala, J., Heintz, U., Hogan, J. M., Kwok, K. H. M., Laird, E., Landsberg, G., Lee, J.,Mao, Z., Narain, M., Sagir, S., Syarif, R., Usai, E., Yu, D., Band, R., Brainerd, C., Breedon, R., Calderon De La Barca Sanchez, M., Chertok, M., Conway, J., Conway, R., Cox, P. T., Erbacher, R., Flores, C., Funk, G., Jensen, F., Ko, W., Kukral, O., Lander, R., Mulhearn, M., Pellett, D., Pilot, J., Shi, M., Stolp, D., Taylor, D., Tos, K., Tripathi, M., Wang, Z., Zhang, F., Bachtis, M., Bravo, C., Cousins, R., Dasgupta, A., Florent, A., Hauser, J., Ignatenko, M., Mccoll, N., Regnard, S., Saltzberg, D., Schnaible, C., Valuev, V., Burt, K., Clare, R., Gary, J. W., Shiasi Shirazi, S. M. A., Hanson, G., Karapostoli, G., Kennedy, E., Long, O. R., Olmedo Negrete, M., Paneva, M. I., Si, W., Wang, L., Wei, H., Wimpenny, S., Yates, B. R., Zhang, Y., Branson, J. G., Chang, P., Cittolin, S., Derdzinski, M., Gerosa, R., Gilbert, D., Hashemi, B., Klein, D., Krutelyov, V., Letts, J., Masciovecchio, M., May, S., Padhi, S., Pieri, M., Sharma, V., Tadel, M., Würthwein, F., Yagil, A., Zevi Della Porta, G., Amin, N., Bhandari, R., Campagnari, C., Citron, M., Dutta, V., Franco Sevilla, M., Gouskos, L., Incandela, J., Marsh, B., Mei, H., Ovcharova, A., Qu, H., RIchman, J., Sarica, U., Stuart, D., Wang, S., Yoo, J., Anderson, D., Bornheim, A., Lawhorn, J. M., Lu, N., Newman, H. B., Nguyen, T. Q., Pata, J., Spiropulu, M., Vlimant, J. R., Xie, S., Zhang, Z., Zhu, R. Y., Andrews, M. B., Ferguson, T., Mudholkar, T., Paulini, M., Sun, M., Vorobiev, I., Weinberg, M., Cumalat, J. P., Ford, W. T., Johnson, A., MacDonald, E., Mulholland, T., Patel, R., Perloff, A., Stenson, K., Ulmer, K. A., Wagner, S. R., Alexander, J., Chaves, J., Cheng, Y., Chu, J., Datta, A., Frakenthal, A., Mcdermott, K., Mirman, N., Patterson, J. R., Quach, D., Rinkevicius, A., Ryd, A., Tan, S. M., Tao, Z., Thom, J., Wittich, P., Zientek, M., Abdullin, S., Albrow, M., Alyari, M., Apollinari, G., Apresyan, A., Apyan, A., Banerjee, S., Bauerdick, L. A. T., Beretvas, A., Berryhill, J., Bhat, P. C., Burkett, K., Butler, J. N., Canepa, A., Cerati, G. B., Cheung, H. W. K., Chlebana, F., Cremonesi, M., Duarte, J., Elvira, V. D., Freeman, J., Gecse, Z., Gottschalk, E., Gray, L., Green, D., Grünendahl, S., Gutsche, O., Hall, A.-R., Hanlon, J., Harris, R. M., Hasegawa, S., Heller, R., Hirschauer, J., Hu, Z., Jayatilaka, B., Jindariani, S., Johnson, M., Joshi, U., Klima, B., Kortelainen, M. J., Kreis, B., Lammel, S., Lewis, J., Lincoln, D., Lipton, R., Liu, M., Liu, T., Lykken, J., Maeshima, K., Marraffino, J. M., Mason, D., McBRidge, P., Merkel, P., Mrenna, S., Nahn, S., O'Dell, V., Papadimitriou, V., Pedro, K., Pena, C., Rakness, G., Ravera, F., Ristori, L., Schneider, B., Sexton-Kennedy, E., Smith, N., Soha, A., Spalding, W. J., Spiegel, L., Stoynev, S., Strait, J., Strobbe, N., Taylor, L., Tkaczyk, S., Tran, N. V., Uplegger, L., Vaandering, E. W., Vernieri, C., Verzocchi, M., Vidal, R., Wang, M., WEber, H. A., Acosta, D., Avery, P., Bortignon, P., Bourilkov, D., Brinkerhoff, A., Cadamuro, L., Carnes, A., Cherepanov, V., Curry, D., Errico, F., Field, R. D., Gleyzer, S. V., Joshi, B. M., Kim, M., Konigsberg, J., Korytov, A., Lo, K. H., Ma, P., Matchev, K., Menendez, N., Mitselmakher, G., Rosenzweig, D., Shi, K., Wang, J., Wang, S., Zuo, X., Joshi, Y. R., Linn, S., Adams, T., Askew, A., Hagopian, S., Hagopian, V., Johnson, K. F., Khurana, R., Kolberg, T., Martinez, G., Perry, T., Prosper, H., Schiber, C., Yohay, R., Baarmand, M. M., Bhopatkar, V., Hohlmann, M., Noonan, D., Rahmani, M., Saunders, M., Yumiceva, F., Adams, M. R., Apanasevich, L., Berry, D., Betts, R. R., Cavanaugh, R., Chen, X., Dittmer, S., Evdokimov, O., Gerber, C. Er., Hangal, D. A., Hofman, D. J., Jung, K., Mills, C., Roy, T., Tonjes, M. B., Varelas, N., Wang, H., Wang, X., Wu, Z., Zhang, J., Alhusseini, M., Bilki, B., Clarida, W., Dilsiz, K., Durgut, S., Gandrajula, R. P., Haytmyradov, M., Khristenko, V., Köseyan, O. K., Merlo, J.-P., Mestvirishvili, A., Moeller, A., Nachtman, J., Ogul, H., Onel, Y., Ozok, F., Penzo, A., Snyder, C., Tiras, E., Wetzel, J., Blumenfeld, B., Cocoros, A., Eminizer, N., Fehling, D., Feng, L., Gritsan, A. V., Hung, W. T., Maksimovic, P., Roskes, J., Swartz, M., Xiao, M., Baldenegro Barrera, C., Baringer, P., Bean, A., Boren, S., Bowen, J., Bylinkin, A., Isidori, T., Khalil, S., King, J., Kropivnitskaya, A., Majumder, D., Mcbrayer, W., Minafra, N., Murray, M., Rogan, C., Royon, C., Sanders, S., Schmitz, E., Tapia Takaki, J. D., Wang, Q., Williams, J., Duric, S., Ivanov, A., Kaadze, K., Kim, D., Maravin, Y., Mendis, D. R., Mitchell, T., Modak, A., Mohammadi, A., Rebassoo, F., Wright, D., Baden, A., Baron, O., Belloni, A., Eno, S. C., Feng, Y., Ferraioli, C., Hadley, N. J., Jabeen, S., Jeng, G. Y., Kellogg, R. G., Kunkle, J., Mignerey, A. C., Nabili, S., Ricci-Tam, F., Seidel, M., Shin, Y. H., Skuja, A., Tonwar, S. C., Wong, K., Abercrombie, D., Allen, B., Baty, A., Bi, R., Brandt, S., Busza, W., Cali, I. A., D'Alfonso, M., Gomez Ceballos, G., Goncharov, M., Harris, P., Hsu, D., Hu, M., Klute, M., Kovalskyi, D., Lee, Y.-J., Luckey, P. D., Maier, B., Marini, A. C., Mcginn, C., Mironov, C., Narayanan, S., Niu, X., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Sumorok, K., Tatar, K., Velicanu, D., Wang, J., Wang, T. W., Wyslouch, B., Benvenuti, A. C., Chatterjee, R. M., Evans, A., Hansen, P., Hiltbrand, J., Kalafut, S., Kubota, Y., Lesko, Z., Mans, J. Rusack, R., Wadud, M. A., Acosta, J. G., Oliversos, S., Avdeeva, E., Bloom, K., Claes, D. R., Frangmeier, C., Finco, L., Golf, F., Gonzalez Suarez, R., Kamalieddin, R., Kravchenko, I., Siado, J. E., Snow, G. R., Stieger, B., Godshalk, A., Harrington, C., Iashcili, I., Kharchilava, A., Mclean, C., Nguyen, D., Parker, A., Rappoccio, S., Roozbahani, B., Alverson, G., Barberis, E., Freer, C., Daddad, Y., Hortiangtham, A., Madigan, G., Morse, D. M., Orimoto, T., Skinnari, L., Tishelman=Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Bhattacharya, S., Bueghly, J., Gunter, T., Hahn, K. A., Odell, N., Schmitt, M. H., Sung, K., Trovato, M., Velasco, M., Bucci, R., Dev, N., Hildreth, M., Hurtado Anampa, K., Jessop, C., Karmgard, D. J., Lannon, K., Li, W., Loukas, N., Marinelli, N., Mcalister, I., Meng, F., Mueller, C., Musienko, Y., Planer, M., Ruchti, R., Siddireddy, P., Smith, G., Taroni, S., Wayne, M., Wightman, A., Wolf, M., Woodard, A., Alimena, J., Bylsma, B., Durkin, L. S., Flowers, S., Francis, B., Hill, C., Ji, W., Lefeld, A., Ling, T. Y., Winer, . L., Cooperstein, S., Dezoort, G., Elmer, P., Hardenbrook, J., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kwan, S., Lange, D., Lucchini, M. T., Luo, J., Marlow, D., Mei, K., Ojalvo, I., Olsen, J., Palmer, C., Piroué, P., Salfeld-Nebgen, J., Stickland, D., Tully, C., Wang, Z., Malik, S., Norberg, S., Barker, A., Barnes, V. E., Das, S., Gutay, L., Jones, M., Jung, A. W., Khatiwada, A., Mahakud, B., Miller, D. H., Negro, G., Neumeister, N., Peng, C. C., Piperov, S., Qiu, H., Schulte, J. F., Sun, J., Wang, F., Xiao, R., Xie, W., Cheng, T., Dolen, J., Parashar, N., Ecklund, K. M., Freed, S., Geurts, F. J. M., Kilpatrick, M., Kumar, A., Li, W., Padley, B. P., Redjimi, R., Roberts, J., Rorie, J., Shi, W., Stahl Leiton, A. G., Tu, Z., Zhang, A., Bodek, A., de Barbaro, P., Demina, R., Duh, Y. T., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Han, J., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Tan, P., Taus, R., Chiarito, B., Chou, J. P., Gershtein, Y., Halkiadakis, E., Hart, A., Heindl, M., Hughes, E., Kaplan, S., Kyriacou, S., Laflotte, I., Lath, A., Montalvo, R., Nash, K., Osherson, M., Saka, H., Salur, S., Schnetzer, S., Sheffield, D., Somalwar, S., Stone, R., Thomas, S., Thomassen, P., Acharya, H., Delannoy, A. G., Heideman, J., Riley, G., Spanier, S., Bouhali, O., Celik, A., Dalchenko, M., De Mattia, M., Delgado, A., Dildick, S., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Luo, S., Marley, D., Mueller, R., Overton, D., Perniè, L., Rathjens, D., Safonov, A., Akchrin, N., Damgov, J., De Guio, F., Kunori, S., Lamichhane, K., Lee, S. W., Mengke, T., Muthumuni, S., Peltola, T., Undleeb, S., Volobouev, I., Wang, Z., Whitbeck, A., Greene, S., Furrola, A., Janjam, R., Johns, W., Maguire, C., Melo, A., Ni, H., Padken, K., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Verweij, M., Arenton, M. W., Barria, P., Cox, B., Cummings, G., Hirosky, R., Joyce, M., Ledovskoy, A., Neu, C., Tannenwald, B., Wang, Y., Wolfe, E., Xia, F., Harr, R., Karchin, P. E., Poudyal, N., Sturdy, J., Thapa, P., Zaleski, S., Buchanan, J., Caillol, C., Carlsmith, D., Dasu, S., Dy Bruyn, I., Dodd, L., Gomber, B., Grothe, M., Herndon, M., Hervé, A., Hussain, U., Klabbers, P., Lanaro, A., Long, K., Loveless, R., Ruggles, T., Savin, A., Sharma, V., Smith, W. H., & Woods, N. Measurement of the average very forward energy as a function of the track multiplicity at central pseudorapidities in proton-proton collisions at s√=13TeV. Eur. Phys. J. C 79, 893 (2019). https://doi.org/10.1140/epjc/s10052-019-7402-3

    View More

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    Kosaraju, V., Sadeghian, A., Martín-Martín, R., Reid, I., Rezatofighi, S. H., & Savarese, S. (2019). Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks.

    View More

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    Qi, Y., Qin, L., Zhang, S., Huang, Q., & Yao, H. (2019). Robust visual tracking via scale-and-state-awareness. Neurocomputing, 329, 75–85. https://doi.org/10.1016/j.neucom.2018.10.035

    View More

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    Mondal, R., Purkait, P., Santra, S., & Chanda, B. (2019). Morphological networks for image de-raining. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11414 LNCS, 262–275. https://doi.org/10.1007/978-3-030-14085-4_21

    View More

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    Xu, W., Liu, W., Chi, H., Qiu, S., & Jin, Y. (2019). Self-paced learning with privileged information. Neurocomputing, 362, 147–155. https://doi.org/10.1016/j.neucom.2019.06.072

    View More

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    Zhang, P., Liu, W., Lei, Y., & Lu, H. (2019). Hyperfusion-Net: Hyper-densely reflective feature fusion for salient object detection. Pattern Recognition, 93, 521–533. https://doi.org/10.1016/j.patcog.2019.05.012

    View More

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    Zhang, P., Liu, W., Wang, H., Lei, Y., & Lu, H. (2019). Deep gated attention networks for large-scale street-level scene segmentation. Pattern Recognition, 88, 702–714. https://doi.org/10.1016/j.patcog.2018.12.021

    View More

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    Zhang, P., Liu, W., Lei, Y., Lu, H., & Yang, X. (2019). Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion.

    View More

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    Doan, A. D., Latif, Y., Chin, T. J., Liu, Y., Ch’Ng, S. F., Do, T. T., & Reid, I. (2019). Visual Localization under Appearance Change: A Filtering Approach. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945810

    View More

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    Ch’ng, S. F., Khosravian, A., Doan, A. D., & Chin, T. J. (2019). Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion. IEEE International Conference on Intelligent Robots and Systems, 7489–7496. https://doi.org/10.1109/IROS40897.2019.8967643

    View More

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    Cai, Z., & Koltun, V. (2019). Consensus Maximization Tree Search Revisited. Retrieved from https://github.

    View More

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    Gale, W., Oakden-Rayner, L., Carneiro, G., Palmer, L. J., & Bradley, A. P. (2019). Producing radiologist-quality reports for interpretable deep learning. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 1275–1279. https://doi.org/10.1109/ISBI.2019.8759236

    View More

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    Tian, Y., Pu, L. Z. C. T., Singh, R., Burt, A. D., & Carneiro, G. (2019). One-stage five-class polyp detection and classification. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 70–73. https://doi.org/10.1109/ISBI.2019.8759521

    View More

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    Snaauw, G., Gong, D., Maicas, G., Hengel, A. Van Den, Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 802–805. https://doi.org/10.1109/ISBI.2019.8759276

    View More

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    Maicas, G., Snaauw, G., Bradley, A. P., Reid, I., & Carneiro, G. (2019). Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 1057–1060. https://doi.org/10.1109/ISBI.2019.8759402

    View More

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    Collins, J., Howard, D., & Leitner, J. (2019). Quantifying the reality gap in robotic manipulation tasks. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 6706–6712. https://doi.org/10.1109/ICRA.2019.8793591

    View More

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    Johnston, A., & Carneiro, G. (2019). Single View 3D Point Cloud Reconstruction using Novel View Synthesis and Self-Supervised Depth Estimation. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945841

    View More

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    Rowntree, T., Pontecorvo, C., & Reid, I. (2019, December 1). Real-Time Human Gaze Estimation. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945919

    View More

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    Zhang, H., Li, Y., Jiang, Y., Wang, P., Shen, Q., & Shen, C. (2019). Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 5813–5828. https://doi.org/10.1109/TGRS.2019.2902568

    View More

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    Zhou, Y., Ji, R., Sun, X., Su, J., Meng, D., Gao, Y., & Shen, C. (2019). Plenty Is Plague: Fine-Grained Learning for Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2956699

    View More

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    Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019). TasselNetv2: In-field counting of wheat spikes with context-augmented local regression networks. Plant Methods, 15(1), 1–14. https://doi.org/10.1186/s13007-019-0537-2

    View More

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    Tian, Y., Yu, X., Fan, B. †, Wu, F., Heijnen, H., & Balntas, V. (2019). SOSNet: Second Order Similarity Regularization for Local Descriptor Learning.

    View More

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    Hua, M. D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2019). Feature-based recursive observer design for homography estimation and its application to image stabilization. Asian Journal of Control, 21(4), 1443–1458. https://doi.org/10.1002/asjc.2012

    View More

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    Ng, K. W., Mahony, R., & Lau, D. (2019). A dual joystick-trackball interface for accurate and time-efficient teleoperation of cable-driven parallel robots within large workspaces. In Mechanisms and Machine Science (Vol. 74, pp. 391–402). https://doi.org/10.1007/978-3-030-20751-9_33

    View More

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    Zhu, H., Guo, M., Li, H., Wang, Q., & Robles-Kelly, A. (2019). Revisiting Spatio-Angular Trade-off in Light Field Cameras and Extended Applications in Super-Resolution. IEEE Transactions on Visualization and Computer Graphics, 1–1. https://doi.org/10.1109/tvcg.2019.2957761

    View More

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    Kumar, S., Dai, Y., & Li, H. (2019). Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2955131

    View More

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    Zhu, H., Sun, X., Zhang, Q., Wang, Q., Robles-Kelly, A., Li, H., & You, S. (2019). Full View Optical Flow Estimation Leveraged from Light Field Superpixel. IEEE Transactions on Computational Imaging, 1–1. https://doi.org/10.1109/tci.2019.2897937

    View More

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    Liu, L., Li, H., & Dai, Y. (2019). Stochastic attraction-repulsion embedding for large scale image localization. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 2570–2579. https://doi.org/10.1109/ICCV.2019.00266

    View More

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    Zhang, K., Luo, W., Ma, L., Liu, W., & Li, H. (2019). Learning Joint Gait Representation via Quintuplet Loss Minimization.

    View More

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    Zhang, H., Dai, Y., Li, H., & Koniusz, P. (2019). Deep Stacked Hierarchical Multi-patch Network for Image Deblurring.

    View More

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    Zhong, Y., Ji, P., Wang, J., Dai, Y., & Li, H. (2019). Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes.

    View More

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    Cheng, X., Zhong, Y., Dai, Y., Ji, P., & Li, H. (2019). Noise-aware unsupervised deep lidar-stereo fusion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6332–6341. https://doi.org/10.1109/CVPR.2019.00650

    View More

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    View More

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    View More

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    View More

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    View More

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    Santa Cruz, R., Fernando, B., Cherian, A., & Gould, S. (2018). Visual Permutation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol. PP). IEEE. https://doi.org/10.1109/TPAMI.2018.2873701

    View More

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    View More

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    View More

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    View More

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    View More

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    View More

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    View More

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    Qiu, S., Anwar, S., & Barnes, N. (2019). Geometric Feedback Network for Point Cloud Classification. Retrieved from http://arxiv.org/abs/1911.12885

    View More

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    View More

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    View More

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    View More

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    View More

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    View More

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    Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Representation Learning on Unit Ball with 3D Roto-translational Equivariance. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01278-x

    View More

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    Hong, X., Peng, W., Harandi, M., Zhou, Z., Pietikäinen, M., & Zhao, G. (2019). Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing, Communications, and Applications, 15(3s), 1–24. https://doi.org/10.1145/3342227

    View More

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    View More

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    Chin, T. J., Bagchi, S., Eriksson, A., & Van Schaik, A. (2019). Star tracking using an event camera. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 1646–1655. https://doi.org/10.1109/CVPRW.2019.00208

    View More

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    Salahat, E., Asselineau, C. A., Coventry, J., & Mahony, R. (2019). Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms. IECON Proceedings (Industrial Electronics Conference), 2019-October, 763–769. https://doi.org/10.1109/IECON.2019.8927123

    View More

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    Bruggemann, T. S., Ford, J. J., White, G., & Perez, T. (2019). Bushfire emergency response simulation.

    View More

  • Bushfire emergency response uncertainty quantification

    Bruggemann, T., Ford, J. J., White, G., Perez, T., & Power, W. (2019). Bushfire emergency response uncertainty quantification. Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019). The Modelling and Simulation Society of Australia and New Zealand Inc., Australia, pp. 42-48.

    View More

  • Multi-marginal Wasserstein GAN

    Cao, J., Mo, L., Zhang, Y., Jia, K., Shen, C., & Tan, M. (2019). Multi-marginal Wasserstein GAN.

    View More

  • Robot Expressive Motions: A Survey of Generation and Evaluation Methods

    Venture, G., & Kulić, D. (2019). Robot Expressive Motions. ACM Transactions on Human-Robot Interaction, 8(4), 1–17. https://doi.org/10.1145/3344286

    View More

  • Deep Hashing by Discriminating Hard Examples

    Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., & Hancock, E. (2019). Deep hashing by discriminating hard examples. MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 1535–1542. https://doi.org/10.1145/3343031.3350927

    View More

  • New Convex Relaxations for MRF Inference with Unknown Graphs

    Wang, Z., Liu, T., Shi, Q., Pawan Kumar, M., & Zhang, J. (2019). New Convex Relaxations for MRF Inference with Unknown Graphs.

    View More

  • Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks

    Yu, X., Porikli, F., Fernando, B., & Hartley, R. (2019). Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01254-5

    View More

  • Learning to Find Common Objects Across Few Image Collections

    Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., & Hartley, R. (2019). Learning to find common objects across few image collections. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5116–5125. https://doi.org/10.1109/ICCV.2019.00522

    View More

  • Bilinear Attention Networks for Person Retrieval

    Fang, P., Zhou, J., Roy, S., Petersson, L., & Harandi, M. (2019). Bilinear attention networks for person retrieval. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8029–8038. https://doi.org/10.1109/ICCV.2019.00812

    View More

  • Siamese Networks: The Tale of Two Manifolds

    Kumar Roy, S., Harandi, M., Nock, R., & Hartley, R. (n.d.). Siamese Networks: The Tale of Two Manifolds. Retrieved from https://github.com/sumo8291/

    View More

  • Predicting the Future: A Jointly Learnt Model for Action Anticipation

    Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Predicting the future: A jointly learnt model for action anticipation. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5561–5570. https://doi.org/10.1109/ICCV.2019.00566

    View More

  • Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost

    Le, H., Xu, M., Hoang, T., & Milford, M. (2019). Hierarchical encoding of sequential data with compact and sub-linear storage cost. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9823–9832. https://doi.org/10.1109/ICCV.2019.00992

    View More

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    Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., Van Den Hengel, A., & Zhu, W. (2019). Watch, reason and code: Learning to represent videos using program. MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 1543–1551. https://doi.org/10.1145/3343031.3351094

    View More

  • Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression

    Cai, M., Zhan, H., Weerasekera, C. S., Li, K., & Reid, I. (2019). Camera relocalization by exploiting multi-view constraints for scene coordinates regression. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 3769–3777. https://doi.org/10.1109/ICCVW.2019.00469

    View More

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    Li, L., Khan, S., & Barnes, N. (2019). Silhouette-assisted 3D object instance reconstruction from a cluttered scene. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2080–2088. https://doi.org/10.1109/ICCVW.2019.00263

    View More

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    Mao, W., Liu, M., Salzmann, M., & Li, H. (2019). Learning trajectory dependencies for human motion prediction. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9488–9496. https://doi.org/10.1109/ICCV.2019.00958

    View More

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    Gupta, K., Petersson, L., & Hartley, R. (2019). CullNet: Calibrated and pose aware confidence scores for object pose estimation. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2758–2766. https://doi.org/10.1109/ICCVW.2019.00337

    View More

  • Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss

    Yu, X., Tian, Y., Porikli, F., Hartley, R., Li, H., Heijnen, H., & Balntas, V. (2019). Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss.

    View More

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    Khan, S. H., Guo, Y., Hayat, M., & Barnes, N. (2019). Unsupervised primitive discovery for improved 3D generative modeling. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9731–9740. https://doi.org/10.1109/CVPR.2019.00997

    View More

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    Xie, Y., Lu, H., Zhang, J., Shen, C., & Xia, Y. (2019). Deep Segmentation-Emendation Model for Gland Instance Segmentation. https://doi.org/10.1007/978-3-030-32239-7_52

    View More

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    Wang, X., Yin, W., Kong, T., Jiang, Y., Li, L., & Shen, C. (2019). Task-Aware Monocular Depth Estimation for 3D Object Detection. Retrieved from http://arxiv.org/abs/1909.07701

    View More

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    Xing, Y., Ge, Z., Zeng, R., Mahapatra, D., Seah, J., Law, M., & Drummond, T. (2019). Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation. https://doi.org/10.1007/978-3-030-32226-7_84

    View More

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    Yu, F., Shang, J., Hu, Y., & Milford, M. (2019). NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biological Cybernetics. https://doi.org/10.1007/s00422-019-00806-9

    View More

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    Avraham, G., Zuo, Y., & Drummond, T. (2019). Parallel optimal transport gan. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4406–4415. https://doi.org/10.1109/CVPR.2019.00454

    View More

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    Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI (pp. 163–178). https://doi.org/10.1007/978-3-030-13969-8_8

    View More

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    Chen, B., Cao, J., Parra, A., & Chin, T. J. (2019). Satellite pose estimation with deep landmark regression and nonlinear pose refinement. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2816–2824. https://doi.org/10.1109/ICCVW.2019.00343

    View More

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    Xiong, H., Lu, H., Liu, C., Liu, L., Cao, Z., & Shen, C. (2019). From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. Retrieved from https://github.

    View More

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    Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., & Shen, C. (2019). Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network.

    View More

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    Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., & Yan, Y. (2019). Exploiting temporal consistency for real-time video depth estimation *. Retrieved from https://tinyurl.com/STCLSTM

    View More

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    Sarker, C., Mejias, L., Maire, F., & Woodley, A. (2020). Evaluation of the Impact of Image Spatial Resolution in Designing a Context-Based Fully Convolution Neural Networks for Flood Mapping. 1–8. https://doi.org/10.1109/dicta47822.2019.8945888

    View More

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    Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443–443. https://doi.org/10.1038/s42256-019-0094-4

    View More

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    Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

    View More

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    Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

    View More

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    Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. Retrieved from http://arxiv.org/abs/1908.04930

    View More

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    Bian, J.-W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.-M., & Reid, I. (2019). Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video.

    View More

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    Li, X., Ma, C., Wu, B., He, Z., & Yang, M.-H. (2019). Target-Aware Deep Tracking.

    View More

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    Liu, C., Ding, W., Xia, X., Hu, Y., Zhang, B., Liu, J, Zhuang, B. & Guo, G. (2019). RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs. Retrieved from http://arxiv.org/abs/1908.07748

    View More

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    Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. 1–8. https://doi.org/10.1109/dicta47822.2019.8945949

    View More

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    Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Indices matter: Learning to index for deep image matting. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 3265–3274. https://doi.org/10.1109/ICCV.2019.00336

    View More

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    Yin, W., Liu, Y., Shen, C., & Yan, Y. (2019). Enforcing geometric constraints of virtual normal for depth prediction. Retrieved from https://tinyurl.com/

    View More

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    Doan, D., Latif, Y., Chin, T. J., Liu, Y., Do, T. T., & Reid, I. (2019). Scalable place recognition under appearance change for autonomous driving. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9318–9327. https://doi.org/10.1109/ICCV.2019.00941

    View More

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    Zheng, Y., Yu, X., Liu, M., & Zhang, S. (2019). YP.ZHENG ET AL: RESIDUAL MULTISCALE BASED SINGLE IMAGE DERAINING Residual Multiscale Based Single Image Deraining.

    View More

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    Ajanthan, T., Dokania, P., Hartley, R., & Torr, P. (2019). Proximal mean-field for neural network quantization. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 4870–4879. https://doi.org/10.1109/ICCV.2019.00497

    View More

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    Gould, S., Hartley, R., & Campbell, D. (2019). Deep Declarative Networks: A New Hope. Retrieved from http://arxiv.org/abs/1909.04866

    View More

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    Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind Your Neighbours: Image Annotation with Metadata Neighbourhood Graph Co-Attention Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2956-2964

    View More

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    Carneiro, G., Manuel, J., Tavares, R. S., Bradley, A. P., Papa, J. P., Nascimento, J. C., Cardoso, J. S., Lu, Z., & Belagiannis, V. (2019, May 4). Editorial. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, Vol. 7, p. 241. https://doi.org/10.1080/21681163.2019.1594056

    View More

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    Korthals, T., Melnik, A., Leitner, J., & Hesse, M. (n.d.). Multisensory Assisted In-hand Manipulation of Objects with a Dexterous Hand. Retrieved from http://arxiv.org/abs/1612.05424

    View More

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    Stanislas, L., Nubert, J., Dugas, D., Nitsch, J., Suenderhauf, N., Siegwart, R., Cadena, C., & Peynot, T. (2019). Airborne particle classification in LiDAR point clouds using deep learning. Proceedings of the 12th Conference on Field and Service Robotics:Keio University, Japan, pp. 1-14.

    View More

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    Du, Y., Hetherington, N. J., Oon, C. L., Chan, W. P., Quintero, C. P., Croft, E., & MacHiel Van Der Loos, H. F. (2019). Group surfing: A pedestrian-based approach to sidewalk robot navigation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 6518–6524. https://doi.org/10.1109/ICRA.2019.8793608

    View More

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    Doering, M., Liu, P., Glas, D. F., Kanda, T., Kulić, D., & Ishiguro, H. (2019). Curiosity Did Not Kill the Robot. ACM Transactions on Human-Robot Interaction, 8(3), 1–24. https://doi.org/10.1145/3326462

    View More

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    Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). TIMTAM: Tunnel-Image Texturally Accorded Mosaic for Location Refinement of Underground Vehicles With a Single Camera. IEEE Robotics and Automation Letters, 4(4), 4362–4369.

    View More

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    Wu, X., Showiheen, S. A. A., Sun, A. R., Crawford, R., Xiao, Y., Mao, X., & Prasadam, I. (2019). Exosomes extraction and identification. In Methods in Molecular Biology (Vol. 2054, pp. 81–91). Humana Press Inc. https://doi.org/10.1007/978-1-4939-9769-5_4

    View More

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    Ortenzi, V., Controzzi, M., Cini, F., Leitner, J., Bianchi, M., Roa, M. A., & Corke, P. (2019). Robotic manipulation and the role of the task in the metric of success. Nature Machine Intelligence, 1(8), 340–346. https://doi.org/10.1038/s42256-019-0078-4

    View More

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    Nash, W., Powell, C., Drummond, T., & Birbilis, N. (2019). Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning. CORROSION, 3397. https://doi.org/10.5006/3397

    View More

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    Zapotezny-Anderson, P., & Lehnert, C. (2019). Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments. IFAC-PapersOnLine, 52(30), 120–125. https://doi.org/10.1016/j.ifacol.2019.12.508

    View More

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    Shi, Y., Liu, L., Yu, X., & Li, H. (2019). Spatial-Aware Feature Aggregation for Cross-View Image based Geo-Localization.

    View More

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    Zhang, K., Luo, W., Ma, L., & Li, H. (2019). Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse. In AAAI 2019 (pp. 9203–9210). Retrieved from www.aaai.org

    View More

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    Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2019). Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects. Retrieved from http://arxiv.org/abs/1907.06371

    View More

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    View More

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    Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2019). Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2019.2930051

    View More

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    Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., & Reid, I. (n.d.). An Evaluation of Feature Matchers for Fundamental Matrix Estimation. Retrieved from https://jwbian.net/Papers/FM_BMVC19.pdf

    View More

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    Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 353–362. https://doi.org/10.1145/3292500.3330871

    View More

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    Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11954 LNCS, 353–366. https://doi.org/10.1007/978-3-030-36711-4_30

    View More

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    View More

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    Herath, S., Fernando, B., & Harandi, M. (2019). Using temporal information for recognizing actions from still images. Pattern Recognition, 96, 106989. https://doi.org/10.1016/J.PATCOG.2019.106989

    View More

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    Avraham, G., Zuo, Y., Dharmasiri, T., & Drummond, T. (2019). EMPNet: Neural localisation and mapping using embedded memory points. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8119–8128. https://doi.org/10.1109/ICCV.2019.00821

    View More

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    Wei, F., Li, Z., Crawford, R., Xiao, Y., & Zhou, Y. (2019). Immunoregulatory role of exosomes derived from differentiating mesenchymal stromal cells on inflammation and osteogenesis. Journal of Tissue Engineering and Regenerative Medicine, term.2947. https://doi.org/10.1002/term.2947

    View More

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    Mejias, L., & Campoy, P. (2019). Visual controllers for relative positioning in indoor settings. 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019, 1194–1200. https://doi.org/10.1109/ICUAS.2019.8797954

    View More

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    View More

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    "X. Wang, C. Shen, H. Li and S. Xu, ""Human Detection Aided by Deeply Learned Semantic Masks,"" in IEEE Transactions on Circuits and Systems for Video Technology.
    doi: 10.1109/TCSVT.2019.2924912"

    View More

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    Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes. Retrieved from https://arxiv.org/pdf/1906.09433

    View More

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    View More

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    Morrison, D., Corke, P., & Leitner, J. (2019). Learning robust, real-time, reactive robotic grasping. The International Journal of Robotics Research, 027836491985906. https://doi.org/10.1177/0278364919859066

    View More

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    View More

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    View More

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    View More

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    Stoffregen, T., & Kleeman, L. (2019). Event Cameras, Contrast Maximization and Reward Functions: an Analysis.

    View More

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    View More

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    Cai, Z., Chin, T.-J., Bustos, A. P., & Schindler, K. (2019). Practical optimal registration of terrestrial LiDAR scan pairs. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 118–131. https://doi.org/10.1016/j.isprsjprs.2018.11.016

    View More

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    View More

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    Lin, G., Liu, F., Milan, A., Shen, C., & Reid, I. (2019). RefineNet: Multi-Path Refinement Networks for Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2893630

    View More

  • Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss

    Zhang, P., Liu, W., Lu, H., & Shen, C. (2019). Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss. IEEE Transactions on Image Processing, 28(6), 3048–3060. https://doi.org/10.1109/TIP.2019.2893535

    View More

  • Attention Residual Learning for Skin Lesion Classification

    Zhang, J., Xie, Y., Xia, Y., & Shen, C. (2019). Attention Residual Learning for Skin Lesion Classification. IEEE Transactions on Medical Imaging, 38(9), 2092–2103. https://doi.org/10.1109/TMI.2019.2893944

    View More

  • Cardiovascular Diseases

    Verjans J., Veldhuis W.B., Carneiro G., Wolterink J.M., Išgum I., Leiner T. (2019) Cardiovascular Diseases. In: Ranschaert E., Morozov S., Algra P. (eds) Artificial Intelligence in Medical Imaging. Springer, Cham

    View More

  • Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

    Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression.

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  • RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

    Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., & Reid, I. (2019). RGBD based dimensional decomposition residual network for 3D semantic scene completion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 7685–7694. https://doi.org/10.1109/CVPR.2019.00788

    View More

  • Associatively Segmenting Instances and Semantics in Point Clouds

    Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively Segmenting Instances and Semantics in Point Clouds. Retrieved from https://github.com/WXinlong/ASIS.

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  • Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation

    Do, T. T., Hoang, T., Le Tan, D. K., Pham, T., Le, H., Cheung, N. M., & Reid, I. (2019). Binary constrained deep hashing network for image retrieval without manual annotation. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 695–704. https://doi.org/10.1109/WACV.2019.00079

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  • Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

    Yan, Q., Gong, D., Zhang, P., Shi, Q., Sun, J., Reid, I., & Zhang, Y. (2019). Multi-scale dense networks for deep high dynamic range imaging. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 41–50. https://doi.org/10.1109/WACV.2019.00012

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  • CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

    Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning.

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  • Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector

    Hunter, J., Jateff, E., & van den Hengel, A. (2019). Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector. In Coastal Research Library (Vol. 31, pp. 89–101). https://doi.org/10.1007/978-3-030-03635-5_6

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  • Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

    Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation.

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  • Knowledge Adaptation for Efficient Semantic Segmentation

    He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge adaptation for efficient semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 578–587. https://doi.org/10.1109/CVPR.2019.00067

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  • Robust foreground segmentation and image registration for optical detection of GEO objects

    Do, H. N., Chin, T. J., Moretti, N., Jah, M. K., & Tetlow, M. (2019). Robust foreground segmentation and image registration for optical detection of GEO objects. Advances in Space Research, 64(3), 733–746. https://doi.org/10.1016/j.asr.2019.03.008

    View More

  • Accurate Imagery Recovery Using a Multi-Observation Patch Model

    Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate imagery recovery using a multi-observation patch model. Information Sciences, 501, 724–741. https://doi.org/10.1016/j.ins.2019.03.033

    View More

  • Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

    Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. IEEE Transactions on Multimedia, 21(11), 2726–2737. https://doi.org/10.1109/TMM.2019.2908352

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  • Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

    Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 1705–1714. https://doi.org/10.1109/ICCV.2019.00179

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  • Actively Seeking and Learning from Live Data

    Teney, D., & Van Den Hengel, A. (2019). Actively Seeking and Learning from Live Data.

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  • FCOS: Fully Convolutional One-Stage Object Detection

    Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection.

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  • A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

    Do, T. T., Tran, T., Reid, I., Kumar, V., Hoang, T., & Carneiro, G. (2019). A theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 10396–10405. https://doi.org/10.1109/CVPR.2019.01065

    View More

  • Heritage image annotation via collective knowledge

    Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., & Yan, Y. (2019). Exploiting temporal consistency for real-time video depth estimation. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 1725–1734. https://doi.org/10.1109/ICCV.2019.00181

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  • TopNet: Structural Point Cloud Decoder

    Tchapmi, L. P., Kosaraju, V., Rezatofighi, H., Reid, I., & Savarese, S. (2019). TopNet: Structural Point Cloud Decoder. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html

    View More

  • A Generative Adversarial Density Estimator

    Abbasnejad, M. E., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A generative adversarial density estimator. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 10774–10783. https://doi.org/10.1109/CVPR.2019.01104

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  • Event-Based Motion Segmentation by Motion Compensation

    Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., & Scaramuzza, D. (2019). Event-Based Motion Segmentation by Motion Compensation. Retrieved from https://youtu.be/0q6ap

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  • CED: Color Event Camera Dataset

    Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., & Scaramuzza, D. (2019). CED: Color Event Camera Dataset.

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  • Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

    Huang, W., Fan, L., Harandi, M., Ma, L., Liu, H., Liu, W., & Gan, C. (2019). Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks. IEEE Transactions on Image Processing, 28(4), 1773–1782. https://doi.org/10.1109/TIP.2018.2877936

    View More

  • Min-Max Statistical Alignment for Transfer Learning

    Herath, S., Harandi, M., Fernando, B., & Nock, R. (2019). Min-max statistical alignment for transfer learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9280–9289. https://doi.org/10.1109/CVPR.2019.00951

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  • Online near time-optimal trajectory planning for industrial robots

    Kim, J., & Croft, E. A. (2019). Online near time-optimal trajectory planning for industrial robots. Robotics and Computer-Integrated Manufacturing, 58, 158–171. https://doi.org/10.1016/J.RCIM.2019.02.009

    View More

  • Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM

    Hall, W., Schmader, T., Aday, A., & Croft, E. (n.d.). Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM. https://doi.org/10.1177/1948550618772582

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  • Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations

    Radmard, S., Moon, A. & Croft, E.A. Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations. Int J of Soc Robotics 11, 105–121 (2019). https://doi.org/10.1007/s12369-018-0480-9

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  • An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers

    Chan, W. P., Pan, M. K. X. J., Croft, E. A., & Inaba, M. (2019). An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers. International Journal of Social Robotics, 1–20. https://doi.org/10.1007/s12369-019-00546-7

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  • Stable Gaussian process based tracking control of Euler–Lagrange systems

    Beckers, T., Kulić, D., & Hirche, S. (2019). Stable Gaussian process based tracking control of Euler–Lagrange systems. Automatica, 103, 390–397. https://doi.org/10.1016/J.AUTOMATICA.2019.01.023

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  • Expression of Curiosity in Social Robots

    Ceha, J., Chhibber, N., Goh, J., McDonald, C., Oudeyer, P.-Y., Kulić, D., & Law, E. (2019). Expression of Curiosity in Social Robots. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–12. https://doi.org/10.1145/3290605.3300636

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  • Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

    Wilde, N., Kulic, D., & Smith, S. L. (2019). Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions. IEEE Robotics and Automation Letters, 4(2), 1691–1698. https://doi.org/10.1109/LRA.2019.2897342

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  • The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

    Meyer, B. J., & Drummond, T. (2019). The importance of metric learning for robotic vision: Open set recognition and active learning. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2924–2931. https://doi.org/10.1109/ICRA.2019.8794188

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  • Visual SLAM: Why Bundle Adjust?

    Bustos, A. P., Chin, T. J., Eriksson, A., & Reid, I. (2019). Visual SLAM: Why bundle adjust? Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2385–2391. https://doi.org/10.1109/ICRA.2019.8793749

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  • Learning to Adapt for Stereo

    Tonioni, A., Rahnama, O., Joy, T., Stefano, L. Di, Ajanthan, T., & Torr, P. H. S. (2019). Learning to Adapt for Stereo.

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  • Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment

    Zhou, Y., Li, H., & Kneip, L. (2019). Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment. In IEEE Transactions on Robotics (Vol. 35, pp. 184–199). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TRO.2018.2875382

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  • Adversarial spatio-temporal learning for video deblurring

    Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., & Li, H. (2019). Adversarial spatio-temporal learning for video deblurring. IEEE Transactions on Image Processing, 28(1), 291–301. https://doi.org/10.1109/TIP.2018.2867733

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  • Single image deblurring and camera motion estimation with depth map

    Pan, L., Dai, Y., & Liu, M. (2019). Single image deblurring and camera motion estimation with depth map. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 2116–2125). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/WACV.2019.00229

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  • Deep Learning AI for Corrosion Detection

    Nash, W., Drummond, T., & Birbilis, N. (2019, May 15). Deep Learning AI for Corrosion Detection. Retrieved from https://www.onepetro.org/conference-paper/NACE-2019-13267

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  • Picking the right robotics challenge

    Leitner, J. (2019). Picking the right robotics challenge. Nature Machine Intelligence, 1(3), 162–162. https://doi.org/10.1038/s42256-019-0031-6

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  • Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization

    Wu, L., Crawford, R., & Roberts, J. (2019). Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 7360–7366. https://doi.org/10.1109/ICRA.2019.8794384

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  • Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy

    Marmol, A., Banach, A., & Peynot, T. (2019). Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy. IEEE Robotics and Automation Letters, 4(2), 918–925. https://doi.org/10.1109/LRA.2019.2892199

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  • Modular field robot deployment for inspection of dilapidated buildings

    Cordie, T. P., Bandyopadhyay, T., Roberts, J., Dunbabin, M., Greenop, K., Dungavell, R., & Steindl, R. (2019). Modular field robot deployment for inspection of dilapidated buildings. Journal of Field Robotics, rob.21872. https://doi.org/10.1002/rob.21872

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  • On the choice of grasp type and location when handing over an object

    Cini, F., Ortenzi, V., Corke, P., & Controzzi, M. (2019). On the choice of grasp type and location when handing over an object. Science Robotics, 4(27), eaau9757. https://doi.org/10.1126/scirobotics.aau9757

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  • Learning to Fuse Multiscale Features for Visual Place Recognition

    Mao, J., Hu, X., He, X., Zhang, L., Wu, L., & Milford, M. J. (2019). Learning to Fuse Multiscale Features for Visual Place Recognition. IEEE Access, 7, 5723–5735. https://doi.org/10.1109/ACCESS.2018.2889030

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  • LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles

    Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). LookUP: Vision-only real-time precise underground localisation for autonomous mining vehicles. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 1444–1450. https://doi.org/10.1109/ICRA.2019.8794453

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  • Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

    Hausler, S., Jacobson, A., & Milford, M. (2019). Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods. IEEE Robotics and Automation Letters, 4(2), 1924–1931. https://doi.org/10.1109/LRA.2019.2898427

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  • Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

    Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3748–3753. https://doi.org/10.1109/IROS40897.2019.8968525

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  • Semantic–geometric visual place recognition: a new perspective for reconciling opposing views

    Garg, S., Suenderhauf, N., & Milford, M. (2019). Semantic–geometric visual place recognition: a new perspective for reconciling opposing views. The International Journal of Robotics Research, 027836491983976. https://doi.org/10.1177/0278364919839761

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  • Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

    Garg, S., Babu, M. V., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., Drummond, T., & Milford, M. (2019). Look no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 4916–4923. https://doi.org/10.1109/ICRA.2019.8794178

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  • Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

    Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive Importance Learning for Improving Lightweight Image Super-Resolution Network. International Journal of Computer Vision, 128(2), 479–499. https://doi.org/10.1007/s11263-019-01253-6

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  • Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study

    Yan, Y., Tan, M., Tsang, I. W., Yang, Y., Shi, Q., & Zhang, C. (2020). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study. IEEE Transactions on Knowledge and Data Engineering, 32(2), 288–301. https://doi.org/10.1109/TKDE.2018.2882197

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  • One-step adaptive markov random field for structured compressive sensing

    Suwichaya Suwanwimolkul, Lei Zhang, Damith C. Ranasinghe, Qinfeng Shi, One-step adaptive markov random field for structured compressive sensing, Signal Processing, Volume 156, 2019,Pages 116-144, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2018.10.020.

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  • Recovering Faces From Portraits with Auxiliary Facial Attributes

    *Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Recovering Faces From Portraits with Auxiliary Facial Attributes. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 406–415). Waikoloa Village, Hawaii, United States: IEEE. http://doi.org/10.1109/WACV.2019.00049

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  • ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

    Song, X., Wang, P., Zhou, D., Zhu, R., Guan, C., Dai, Y., Su, H., Li, H., & Yang, R. (2019). APOLLOCAR3D: A large 3D car instance understanding benchmark for autonomous driving. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 5447–5457. https://doi.org/10.1109/CVPR.2019.00560

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  • Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

    Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. Medical Image Analysis, 58, 101562. https://doi.org/10.1016/j.media.2019.101562

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  • Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

    Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Hengel, A. Van Den. (2019). Neighbourhood watch: Referring expression comprehension via language-guided graph attention networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1960–1968. https://doi.org/10.1109/CVPR.2019.00206

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  • On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC

    Tran, N.-T., Le Tan, D.-K., Doan, A.-D., Do, T.-T., Bui, T.-A., Tan, M., & Cheung, N.-M. (2019). On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC. IEEE Transactions on Image Processing, 28(4), 1675–1690. http://doi.org/10.1109/TIP.2018.2881829

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  • Object Captioning and Retrieval with Natural Language

    Nguyen, A., Tran, Q. D., Do, T. T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2019). Object captioning and retrieval with natural language. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2584–2592. https://doi.org/10.1109/ICCVW.2019.00316

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  • Visual Question Answering as Reading Comprehension

    Li, H., Wang, P., Shen, C., & Hengel, A. Van Den. (2019). Visual question answering as reading comprehension. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6312–6321. https://doi.org/10.1109/CVPR.2019.00648

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  • Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

    Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured binary neural networks for accurate image classification and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 413–422. https://doi.org/10.1109/CVPR.2019.00050

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  • Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

    Zhang, T., Lin, G., Cai, J., Shen, T., Shen, C., & Kot, A. C. (2019). Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation. IEEE Transactions on Multimedia, 21(11), 2930–2941. https://doi.org/10.1109/TMM.2019.2914870

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  • Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

    Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Fast neural architecture search of compact semantic segmentation models via auxiliary cells. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9118–9127. https://doi.org/10.1109/CVPR.2019.00934

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  • Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

    Morrison, D., Corke, P., & Leitner, J. (2019). Multi-view picking: Next-best-view reaching for improved grasping in clutter. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 8762–8768. https://doi.org/10.1109/ICRA.2019.8793805

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  • Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

    Miller, Di., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2348–2354. https://doi.org/10.1109/ICRA.2019.8793821

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  • Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

    Li, H., Wang, P., Shen, C., & Zhang, G. (2019). Show, attend and read: A simple and strong baseline for irregular text recognition. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 8610–8617. https://doi.org/10.1609/aaai.v33i01.33018610

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  • Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

    Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2019). Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion. IEEE Robotics and Automation Letters, 4(2), 177–184. http://doi.org/10.1109/LRA.2018.2884765

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  • Second-order Temporal Pooling for Action Recognition

    Cherian, A., Gould, S. Second-order Temporal Pooling for Action Recognition. Int J Comput Vis 127, 340–362 (2019). https://doi.org/10.1007/s11263-018-1111-5

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  • Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

    Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119–133. https://doi.org/10.1016/j.patcog.2019.01.006

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  • Memory Efficient Max Flow for Multi-label Submodular MRFs

    Ajanthan, T., Hartley, R., & Salzmann, M. (2019). Memory Efficient Max Flow for Multi-Label Submodular MRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 886–900. https://doi.org/10.1109/TPAMI.2018.2819675

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Book Chapters

  • Reinforcement Learning with Attention that Works: A Self-Supervised Approach

    Manchin, A., Abbasnejad, E., & van den Hengel, A. (2019). Reinforcement Learning with Attention that Works: A Self-Supervised Approach. Communications in Computer and Information Science, 1143 CCIS, 223–230. https://doi.org/10.1007/978-3-030-36802-9_25

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  • Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI

    Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI (pp. 163–178). https://doi.org/10.1007/978-3-030-13969-8_8

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  • Exosomes Extraction and Identification

    Wu, X., Showiheen, S. A. A., Sun, A. R., Crawford, R., Xiao, Y., Mao, X., & Prasadam, I. (2019). Exosomes extraction and identification. In Methods in Molecular Biology (Vol. 2054, pp. 81–91). Humana Press Inc. https://doi.org/10.1007/978-1-4939-9769-5_4

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  • Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose

    Cerón, A., Prieto, F., & Mejias, L. (2019). Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose. In Path Planning for Autonomous Vehicles - Ensuring Reliable Driverless Navigation and Control Maneuver. https://doi.org/10.5772/intechopen.86689

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Journal Articles

  • BTEL: A Binary Tree Encoding Approach for Visual Localization

    Le, H., Hoang, T., & Milford, M. J. (2019). BTEL: A Binary Tree Encoding Approach for Visual Localization. IEEE Robotics and Automation Letters, 4(4), 4354–4361. https://doi.org/10.1109/LRA.2019.2932580

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K., Luetic, J., Popov, A., Postiau, N., Starling, E., Thomas, L., Vander Velde, C., Vanlaer, P., Vannerom, D., Wang, Q., Cornelis, T., Dobur, D., Fagot, A., Gul, M., Khvastunov, I., Roskas, C., Trocino, D., Tytgat, M., Verbeke, W., Vermassen, B., Vit, MZaganidis, N., Bondu, O., Bruno, G., Caputo, C., David, P., Delaere, C., Delcourt, M., Giammanco, A., Vischia, P., Zobec, J., Alves, F. L., Alves, G. A., Correia Silva, G., Hansel, C., Moraes, A., Rebello Teles, P., Belchior Batista Das Chagas, E., Carvalho, W., Chinellato, J., Coelho, E., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., De Oliveira Martins, C., Fonseca De Souza, S., Huertas Guativa, L. M., Malbouisson, H., Matos Figueiredo, D., Medina Jaime, M., Melo De Almeida, M., Mora Herrera, C., Mundim, L., Nogima, H., Prado Da Silva, W. L., Sanchez Rosas, L. J., Santoro, A., Sznajder, A., Thiel, M., Tonelli Manganote, E. J., Torres Da Silva De Araujo, F., Vilela Pereira, A., Ahuja, S., Bernardes, C. A., Calligaris, L., De Souza Lemos, D., Fernandez Perez Tomei, T. R., Gregores, E. M., Mercadante, P. G., Novaes, S. F., Padula, S., Aleksandrov, A., Antchev, G., Hadjiiska, R., Laydjiev, P., Marinov, A., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Fang, W., Gao, X., Yuan, L., Ahmad, M., Chen, G. M., Chen, H. S., Chen, M., Jiang, C. H., Leggat, D., Liao, H., Liu, Z., Shaheen, S. M., Spiezia, A., Tao, J., Yazgan, E., Zhang, H., Zhang, S., Zhao, J., Agapitos, A., Ban, Y., Chen, G., Levin, A., Li, J., Li, L., Li, Q., Mao, Y., Qian, S. J., Wang, D., Wang, Y., Avila, C., Cabrera, A., Chaparro Sierra, L. F., Florez, C., González Hernández, C. F., Segura Delgado, M. A., Ruiz Alvarez, J. D., Giljanović,D., Godinovic, N., Lelas, D., Puljak, I., Sculac, T., Antunovic, Z., Kovac, M., Brigljevic, V., Ferencek, D., Kadija, K., Mesic, B., Roguljic, M., Starodumov, A., Susa, T., Ather, M. W., Attikis, A., Erodotou, E., Ioannou, A., Kolosova, M., Konstantinou, S., Mavromanolakis, G., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Tsiakkouris, D., Finger, M., Finger Jr, M., Ayala, E., Carrera Jarrin, E., Mahmoud, M. A., Mahammad, Y., Bhowmik, S., Carvalho Antunes De Oliveira, A., Dewanjee, R. K., Ehataht, K., Kadastik, M., Raidal, M., Veelken, C., Eerola, P., Kirschenmann, H., Osterberg, K., Pekkanen, J., Voutilainen, M., Garcia, F., Havukainen, J., Heikkilä, J. K., Järvinen, J., Karimäki, V., Kinnunen, R., Lampén, T., Lassila-Perini, K., Laurila, S., Lehti, S., Lindén, T., Luukka, P., Mäenpää, T., Siikonen, H., Tuominen, E., Tuominiemi, J., Tuuva, T., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Fabbro, B., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., Hamel de Monchenault, G., Jarry, P., Leloup, C., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Titov, M., Amendola, C., Beaudette, F., Busson, P., Charlot, C., Diab, B., Granier de Cassagnac, R., Kucher, I., Lobanov, A., Martin Perez, C., Nguyen, M., Ochando, C., Paganini, P., Rembser, J., Salerno, R., Sauvan, J. B., Sirois, Y., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Bloch, D., Bourgatte, G., Brom, J.-M., Chabert, E. C., Collard, C., Conte, E., Fontaine, J.-C., Gelé, D., Goerlach, U., Jansová,M., Le Bihan, A.-C., Tonon, N. Van Hove, P., Gadrat, S., Beauceron, S., Bernet, C., Boudoul, G., Camen, C., Chanon, N., Chierici, R., Contardo, D., Depasse, P., El Mamouni, H., Fay, J., Gascon, S., Gouzavitch, M., Ille, B., Jain, Sa., Lagarde, F., Laktineh, I. B., Lattaud, H., Lethuillier, M., Mirabito, L., Perriees, S., Sordini, V., Touquet, G., Vander Donckt, M., Viret, S., Khvedelidze, A., Tsamalaidze, Z., Autermann, C., Feld, L., Kiesel, M. K., Klein, K., Lipinski, M., Meuser, D., Pauls, A., Preuten, M., Rauch, M. 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M., Diez Pardos, C., Domínguez Damiani, D., Eckerlin, G., Eckstein, D., Eichhorn, T., Elwood, A., Eren, E., Gallo, E., Geiser, A., Grados Luyando, J. M., Grohsjean, A., Guthoff, M., Haranko, M., Harb, A., Jomhari, N. Z., Jung, H., Kasem, A., Kasemann, M., Keaveney, J., Kleinwort, C., Knolle, J., Krücker, D., Lange, W., Lenz, T., Leonard, J., Lidrych, J., Lipka, K., Lohmann, W., Mankel, R., Melzer-Pellmann, I.-A., Meyer, A. B., Meyer, M., Missiroli, M., Mittag, G., Mnich, G., Mcich, J., Mussgiller, A., Myronenko, V., Pérez Adán, D., Pflitsch, S. K., Pitzl, D., Raspereza, A., Saibel, A., Savitskyi, M., Scheurer, V., Schütze, P., Schwanenberger, C., Shevchenko, R., Singh, A., Tholen, H., Turkot, O., Vagnerini, A., Van De Klundert, M., Van Onsem, G. P., Walsh, R., Wen, Y., Wichmann, K., Wissing, C., Zenaiev, O., Zlebcik, R., Aggleton, R., Bein, S., Benato, L., Benecke, A., Blobel, V., Dreyer, T., Ebrahimi, A., Fröhlich, A., Garbers, C., Garutti, E., Gonzalez, D., Gunnellini, P., Haller, J., Hinzmann, A., Karavdina, A., Kasieczka, G., Klanner, R., Kogler, R., Kovalchuk, N., Kurz, S., Kutzner, V., Lange, J., Lange, T., Malara, A., Marconi, D., Multhaup, J., Niedziela, M., Niemeyer, C. E. N., Nowatschin, D., Perieanu, A., Reimers, A., Rieger, O., Scharf, C., Schleper, P., Schumann, S., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrück, G., Stober, F. M., Stöver, M., Cormwald, B., Zoi, I., Akbiyik, M., Barth, C., Baselga, M., Baur, S., Berger, T., Butz, E., Caspart, R., Chwalek, T., De Boer, W., Dierlamm, A., El Morabit, K., Faltermann, N., Giffels, M., Goldenzweig, P., Harrendorf, M. A., Hartmann, F., Husemann, U., Kudella, S., Mitra, S., Mozer, M. U., Müller, Th., Musich, M., Nürnberg, A., Quast, G., Rabbertz, K., Schröder, M., Shvetsov, I., Simonis, H. J., Ulrich, R., Weber, M., Wöhrmann, C., Wolf, R., Anagnostou, G., Asenov, P., Daskalakis, G., Geralis, T., Kyriakis, A., Loukas, D., Paspalaki, G., Diamantopoulou, M., Karathanasis, G., Kontaxakis, P., Panagiotou, A., Papavergou, I., Saoulidou, N., Theofilatos, K., Vellidis, K., Bakas, G., Kousouris, K., Papakrivopoulos, I., Tsipolitis, G., Evangelou, I., Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Mallios, S., Manitara, K., Manthos, N., Papadopoulos, I., Paradas, E., Strologas, J., Triantis, F. A., Tsitsonis, D., Bartók, M., Csanad, M., Major, P., Mandel, K., Mehta, A., Nagy, M. I., Pasztor, G., Surányi, O., Veres, G. I., Bencze, G., Hajdu, C., Horvath, D., Hunyadi, Ã., Sikler, F., Vámi, T. Ã., Veszpremi, V., Vesztergombi, G., Beni, N., Czellar, S., Karancsi, J., Makovec, A., Molnar, J., Szillasi, Z., Raics, P., Teyssier, D., Trocsanyi, Z. L., Ujvari, B., Csorgo, T. F., Nemes, F., Novak, T., Choudhury, S., Komaragiri, J. R., Tiwari, P. C., Bahinipati, S., Kar, C., Kole, G., Mal, P., Muraleedharan Nair Bindu, V. K., Nayak, A., Roy Chowdhury, S., Sahoo, D. K., Swain, S. K., Bansal, S., Beri, S. B., Bhatnagar, V., Chauhan, S., Chawla, R., Dhingra, N., Gupta, R., Kaur, A., Kaur, M., Karu, S., Kumari, P., Lohan, M., Meena, M., Sandeep, K., Sharma, S., Singh, J. B., Virdi, A. K., Walia, G., Bhardwaj, A., Choudhary, B. C., Garg, R. B., Gola, M., Keshri, S., Kumar, A., Malhotra, S., Naimuddin, M., Priyanka, P., Ranjan, K., Shah, A., Sharma, R., Bhardwaj, R., Bharti, M., Bhattacharya, R., Bhattacharya, S., Bhawandeep, U., Bhowmik, D., Dey, S., Dutta, S., Ghosh, S., Maity, M., Mondal, K., Nandan, S., Purohit, A., Rout, P. K., Rou, A., Saha, G., Sarkar, S., Sarkar, T., Sharan, M., Singh, B., Thakur, S., Behera, P. K., Muhammad, A., Chudasama, R., Dutta, D., Jha, V., Kumar, V., Mishra, D. K., Netrakanti, P. K., Pant, L. M., Shukla, P., Aziz, T., Bhat, M. A., Dugad, S., Mohanty, G. B., Sur, N., Kumar Verman, R., Banerjee, S., Bhattacharya, S., Chatterjee, S., Das, P., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Sahoo, N., Sawant, S., Chauhan. S., Dube, S., Hegde, V., Kapoor, A., Kothekar, K., Pandey, S., Rane, A., Rastogi, A., Sharma, S., Chenarani, S., Eskandari Tadavani, E., Etesami, S. M., Khakzad, M., Mohammadi Najafabadi, M., Naseri, M., Rezaei Hosseinabadi, F., Safarzadeh, B., Fecini, M., Grunewald, M., Abbrescia, M., Calabria, C., Colaleo, A., Creanza, D., Cristella, L., De Filippis, N., De Palma, M., Di Florio., Fiore, L., Gelmi, A., Iaselli, G., Ince, M., Lezki, S., Maggi, G., Maggi, M., Miniella, G., My, S., Nuzzo, S., Pompili, A., Pugliese, G., Radogna, R., Ranieri, A., Selvaggi, G., Silvestris, L., Venditti, R., Verqilligen, P., Abbiendi, G., Battilana, C., Bonacorsi, D., Borgonovi, L., Braibant-Giacomelli, S., Campanini, R., Capiluppi, P., Castro, A., Cavallo, F. R., Ciocca, C., Codispoti, G., Cuffiani, M., Dallavalle, G. M., Gabbri, F., Fanfani, A., Fontanesi, E., Giacomelli, P., Grandi, C., Guiducci, L., Iemmi, F., Lo Meo, S., Marcellini, S., Masetti, G., Navarria, F. L., Perrotta, A., Primavera, F., Rossi, A. M., Rovelli, T., Siroli, G. P., Tosi, N., Albergo, S., Costa, S., Di Mattia, A., Potenza, R., Tricomi, A., Tuve, C., Barbagli, G., Ceccarelli, R., Chatterjee, K., Ciulli, V., Civinini, C., D'Alessandra, R., Focardi, E., Latina, G., Lenzi, P., Meschini, M., Paoletti, S., Russo, L., Sguazzoni, G., Strom, D., Viliani, L., Benussi, L., Bianco, S., Fabbri, F., Piccolo, D., Bozzo, M., Ferro, F., Mulargia, R., Robutti, E., Tosi, S., Benaglia, A., Beschi, A., Brivio, F., Ciriolo, V., Di Guida, S., Dinardo, M. E., Dini, P., Fiorendi, S., Gennai, S., Ghezzi, A., Govoni, P., Malberti, M., Malvezzi, S., Menasce, D., Monti, F., Moroni, L., Ortona, G., Paganoni, M., Pedrini, D., Ragazzi, S., Tabarelli de Fatis, T., Zuolo, D., Buontempo, S., Cavallo, N., De Lorio, A., Di Crescenzo, A., Fabozzi, F., Fienga, F., Galati, G., Iorio, A. O. M., Lista, L., Meola, S., Paolucci, P., Rossi, B., Sciacca, C., Voevodina, E., Azzi, P., Bacchetta, N., Bisella, D., Boletti, A., Bragagnolo, A., Carlin, R., Checchia, P., Dall'Osso, M., De Castro Manzano, P., Dorigo, T., Dosselli, U., Gasparini, F., Gasparini, U., Gozzelino, A., Hoh, S. Y., Lujan, P., Margoni, M., Meneguzzo, A. T., Pazzini, J., Presilla, M., Ronchese, P., Rossin, R., Simonetto, F., TIko, A., Torassa, E., Tosi, M., Zanetti, M., Zotto, P., Zumerle, G., Braghieri, A., Montagna, P., Ratti, S. P., Re, V., Ressegotti, M., Riccardi, C., Salvini, P., Vai, I., Vitulo, P., Biasini, M., Bilei, G. M., Cecchi, C., Ciangottini, D., Fanò, L., Lariccia, P., Leonardi, R., Manoni, E., Mantovani, G., Mariani, V., Menichelli, M., Rossi, A., Santocchia, A., Spiga, D., Androsov, K., Azzurri, P., Bagliesi, G., Bertacchi, V., Bianchini, L., Boccali, T., Castaldi, R., Ciocci, M. A., Dell'Orso, R., Fedi, G., Fiori, F., Giannini, L., Giassi, A., Grippo, M. T., Ligabue, F., Manca, E., Mandorli, G., Messineo, A., Palla, F., Rizzi, A., Rolandi, G., Scribano, A., Spagnolo, P., Tenchini, R., Tonelli, G., Turini, N., Venturi, A., Verdini, P. G., Cavallari, F., Cipriani, M., Del Re, D., Di Marco, E., Diemoz, M., Gelli, S., Longo, E., Marzocchi, B., Meridiani, P., Organtini, G., Pandolfi, F., Paramatti, R., Preiato, F., Quaranta, C., Rahatlou, S., Rovelli, C., Santanastasio, F., Soffi, L., Amapane, N., Arcidiacono, R., Argiro, S., Arneodo, M., Bartosik, N., Bellan, R., Biino, C., Cappati, A., Cartiglia, N., Cartiglia, N., Cenna, F., Cometti, S., Costa, M., Covarelli, R., Demaria, N., Kiani, B., Mariotti, C., Maselli, S., Migliore, E., Monaco, V., Montiel, E., Monteno, M., Obertino, M. M., Pacher, L., Pastronie, N., Pelliccioni, M., Pinna Angioni, G. L., Romero, A., Ruspa, M., Sacchi, R., Salvatico, R., Shchelina, K., Sola, V., Solano, A., Soldi, D., Staiano, A., Belforte, S., Candelise, V., Casarsa, M., Cossutti, F., Da Rold, A., Della Ricca, G., Vazzoler, F., Zanetti, A., Kim, B., Kim, D. H., Kim, G. N., Kim, M. S., Lee, J., Lee, S. W., Moon, C. S., Oh, Y. D., Pak, S. I., Sekmen, S., Son, D. C., Yang, Y. C., Kim, H., Moon, D. H., Oh, G., Francois, B., Kim, T. J., Park, J., Cho, S., Choi, S., Go, Y., Gyun, D., Ha, S., Hong, B., Jo, Y., Lee, K., Lee, K. S., Lee, S., Lim, J., Park, J., Park, S. K., Roh, Y., Goh, J., Kim, H. S., Almond, J., Bhyun, J. H., Choi, J., Jeon, S., Kim, J., Kim, J. S., Lee, H., Lee, K., Lee, S., Nam, K., Oh, S. B., Radburn-Smith, B. C., Seo, S. H., Yang, U. K., Yoo, H. D., Yoon, I., Yu, G. B., Jeon, D., Kim, H., Kim, J. H., Lee, J. S. H., Park, I. C., Choi, Y., Hwang, C., Jeong, Y., Lee, J., Lee, Y., Yu, I., Veckalns, V., Dudenas, V., Juodagalvis, A., Vaitkus, J., Ibrahim Z. A., Mohamad Idris, F., Wan Abdullah, W. A. T., Yusli, M. N., Zolkapli, Z., Benitez, J. F., Castaneda, Hernandez, A., Murillo Quijada, J. A., Valencia Palomo, L., Castilla-Valdez, H., De La Cruz-Burelo, E., Duran-Osuna, M. C., Heredia-De La Crus, I., Lopez-Fernandez, R., Rabadan-Trejo, R. I., Ramirez-Sanchez, G., Reyes-Almanza, R., Sanchez-Hernandez, A., Carrillo Moreno, C., Ramirez-Garcia, M., Vazquez Valencia, F., Eysermans, J., Pedraza, I., Salazar Ibarguen, H. A., Uribe Estrada, C., Morelos Pineda, A., Raicevic, N., Krofcheck, D., Bheesette, S., Butler, P. H., Ahmad, A., Ahmad, M., Hassan, Q., Hoorani, H. R., Khan, W. A., Shah, M. A., Shoaib, M., Wagas, M., Avati, V., Grzanka, L., Malawski, M., Bialkowska, H., Bluj, M., Boimska, B., Górski, M., Kazana, M., Szleper, M., Zalewski, P., Bunkowski, K., Byszuk, A., Doroba, K., Kalinowski, A., Konecki, M., Krolikowski, J., Misiura, M., Olszewski, M., Pyskir, A., Walczak, M., Araujo, M., Bargassa, P., Bastos, D., Di Francesco, A., Faccioli, P., Galinhas, B., Gallinaro, M., Hollar, J., Leonardo, N., Seizas, J., Strong, G., Toldaiev, O., Varela, J., Alexakhin, V., Bunin, P., Ershov, Y., Gavrilenko, M., Golutvin, I., Kamenev, A., Karjavine, V., Kashunin, I., Korenkov, V., Kozlov, G., Lanev, A., Malakhov, A., Matveev, V., Moisenz, P., Palichik, V., Perelygin, V., Shmatov, S., Shulha, S., Yuldashev, B. S., Zarubin, A., Chtchipounov, L., Golovtsov, V., Ivanov, Y., Kim, V,., Kuznetsova, E., Levchenko, P., Murzin, V., Oreshkin, V., Smirnov, I., Sosnov, D., Sulimov, V., Uvarov, L., Vorobyev, A., Andreev, Yu., Dermenev, A., Gninenko, S., Golubev, N., Karneyeu, A., Kirsanov, M., Krasnikov, N., Pashenkov, A., Tlisov, D., Toropin, A., Epshteyn, V., Gavrilov, V., Lychkovskaya, N., Nikitenko, A., Popov, V., Pozdnyakov, I., Safronov, G., Spiridonov, A., Stepannov, A., Stolin, V., Toms, M., Vlasov, E,., Zhokin, A., Aushev, T., Chadeeva, M., Philippov, D., Popova, E., Rusinov, V., Andreev, V., Azarkin, M., Dremin, I., Kirakosyan, M., Terkulov, A., Belyaev, A., Boos, E., Ershov, A., Gribushin, A., Khein, L., Klyukhin, V., Kodolova, O., Lokhtin, I., Lukina, O., Obraztsov, S., Petrushanko, S., Savrin, V., Snigirev, A., Barnyakov, A., Blinov, V., Dimova, T., Kardapoltsev, L., Skovpen, Y., Azhgirey, I., Bayshev, I., Bitioukov, S., Kachanov, V., Konstantinov, D., Mandrik, P., Petrov, V., Ryutin, R., Slabospitskii, S., Sobol, A., Troshin, S., Tyurin, N., Uzunian, A., Volkov, A., Babaev, A., Iuzhakov, A., Okhotnikov, V., Borchsh, V., Ivantchenko, V., Tcherniaev, E., Adzic, P., Cirkovic, P., Devetak, D., Dordevic, M., Milenovic, P., Milosevic, J., Stojanovic, M., Aguilar-Benitez, M., Alcaraz Maestre, J., Ãlvarez Fernández, A., Bachiller, I., Barrio Luna, M., Brochero Cifuentes, J. A., Carrillo Montoya, C. A., Cepeda, M., Cerrada, M., Colino, N., De La Cruz, B., Delgado Peris, A., Fernandez Bedoya, C., Fernández Ramos, J. P., Flix, J., Frouz, M. C., Gonzalez Lopez, O., Goy Lopez, S., Hernandez, J. M., Josa, M. I., Moran, D., Navarro Tobar, Ã., Pérez-Calero Yzquierdo, A., Puerta Pelayo, J., Redondo, I., Romero, L., Sánchez Navas, S., Soares, M. S., Triossi, A., Willmott, C., Albajar, C., de Trocóniz, J. F., Cuevas, J., Erice, C., Fernandez Menendez, J., Folgueras, S., Gonzalez Caballero, I., González Fernández, J. R., Palencia Cortezon, E., Rodríguez Bouza, V., Sanchez Cruz, S., Vizan Garcia, J. M., Cabrillo, I. J., Calderon, A., Chazin Quero, B., Duarte Campderros, J., Fernandez, M., Fernández Manteca, P. J., García Alonso, A., Gomez, G., Martinez Rivero, C., Martinez Ruiz del Arbol, P., Matorras, F., Piedra Gomez, J., Prieels, C., Rodrigo, T., Ruiz-Jimeno, A., Scodellaro, L., Trevisani, N., Vila, I., Malagalage, K., Dharmaratna, W. G. D., Wickramage, N., Abbaneo, D., Akgun, B., Auffray, E., Auzinger, G., Baechler, J., Baillon, P., Ball, A. H., Barney, D., Bendavid, J., Bianco, M., Bocci, A., Bossini, E., Botta, C., Brondolin, E., Caporesi, T., Caratelli, A., Cerminara, G., Chapon, E., Cucciati, G., d'Enterria, D., Dabrowski, A., Daci, N., Daponte, V., David, A., De Roeck, A., Deelen, A., Deelen, N., Deile, M., Dobson, M., Dünser, M., Dunpont, N., Elliott-Peisert, A., Fallavollita, F., Fasanella, D., Franzoni, G., Fulcher, J., Funk, W., Giani, S., Gigi, D., Gilbert, A., Gill, K., Glege, F., Gruchala, M., Guilbaud, M., Gulhan, D., Hegeman, J., Heidegger, C., Iiyana, Y., Innocente, V., Jarafi, A., Janot, P., Karacheban, O., Kaspar, J., Kieseler, J., Krammer, M., Lange, C., Lecoq, P., Lourenco, C., Melgeri, L., Mannelli, M., Massironi, A., Meijers, F., Merlin, J. A., Mersi, S., Meschi, E., Moortgat, F., Mulders, M., Ngadiuba, J., Nourbakhsh, S., Orfanelli, S., Orsini, L., Pantaleo, F., Pape, L., Perez, E., Peruzzi, M., Petrilli, A., Petrucciani, G., Pfeiffer, A., Pierini, M., Pitters, F. M., Quinto, M., Rabady, D., Racz, A., Rovere, M., Sakulin, H., Schäfer, C., Schwick, C., Selvaggi, M., Sharma, A., Silva, P., Snoeys, W., Sphicas, P., Stakia, A., Steggemann, J., Tavolaro, V. R., Treille, D., Tsirou, A., Vartak, A., Verzetti, M., Zeuner, W. D., Caminada, L., Deiters, K., Erdmann, W., Horisberger, R., Ingram, Q., Kaestli, H. C., Kotlinski, D., Langenegger, U., Rohe, T., Wiederkehr, S. A., Backhaus, M., Berger, P., Chernyavskaya, N., Dissertori, G., Dittmar, M., Donegà, M., Dorfer, C., Gómez Espinosa, T. A., Grab, C., Hits, C., Klijnsma, T., Lustermann, W., Manzoni, R. A., Marionneau, M., Meinhard, M. T., Micheli, F., Musella, P., Nessi-Tedaldi, F., Pauss, F., Perrin, G., Perrozzi, L., Pigazzini, S., Reichmann, M., Reissel, C., Reitenspiess, T., Ruini, D., Sanz Becerra, D. A., Schönenberger, M., Shchutska, L., Vesterbacka Olsson, M. L., Wally, R., Zhu, D. H., Aarrestad, T. K., Amsler, C., Brzhechko, D., Canelli, M. F., De Cosa, A., Del Burgo, R., Donato, S., Galloni, C., Kilminster, B., Leontsinis, S., Mikuni, V. M., Neutelings, I., Rauco, G., Robmann P., Salerno, D., Schweiger, K., Seitz, C., Takahashi, Y., Wertz, S., Zucchetta, A., Doan, T. H., Kuo, C. M., Lin, W., Yu, S. S., Chang, P., Chao, Y., Chen, K. F., Chen, P. H., Hou, W.-S., Li, Y. Y., Lu, R.-S., Paganis, E., Psallidas, A., Steen, A., Asavapibhop, B., Srimanobhas, N., Suwonjandee, N., Bat, A., Boran, F., Cerci, S., Damarseckin, S., Demiroglu, Z. S., Dolek, F., Dozen, C., Dumanoglu, I., Gokbulut, G., Guler, E. G., Fuler, Y., Hos, I., Isik, C., Kangal, E. E., Kara, O., Kayis Topaksu, A., Kiminsu, U., Oglakci, M., Onengut, G., Ozdemir, K., Ozturk, S., Simsek, A. E., Sunar Cerci, D., Tok, U. G., Turkcapar, S., Zorbakir, I. S., Zorbilmez, C., Isildak, B., Karapinar, G., Yalvac, M., Ataksis, I. O., Gülmez, E., Kaya, M., Kaya, O., Kaynak, B., Özçelik, Ö., Ozkorucuklu, S., Tekten, S., Tekten, S., Yetkin, E. A., Cakir, A., Komurcu, Y., Sen, S., Grynyov, B., Levchuk, L., Ball, F., Bhal, E., Bologna, S., Brooke, J. J., Burns, D., Clement, E., Cussans, D., Davignon, O., Flacher, H., Goldstein, J., Heath, G. P., Heath, H. F., Kreczko, L., Paramesvaran, S., Penning, B., Sakuma, T., Seif El Nasr-Storey, S., Smith, D., Smith, V. J., Taylor, J., Titterton, A., Bell, K. W., Belyaev, A., Brew, C., Brown, R. M., Cieri, D., Cockerill, D. J. A., Coughlan, J. A., Harder, K., Harper, S., Linacre, J., Manolopoulos, K., Newbold, D. M., Olaiya, E., Petyt, D., Reis, T., Schuh, T., Shepherd-Themistocleous, C. H., Thea, A., Tomalin, I. R., Williams, T., Womersley, W. J., Bainbridge, R., Bloch, P., Borg, J., Breeze, S., Buchmuller, O., Bundock, A., Singh Chahal, G., Colling, D., Dauncey, P., Davies, G., Della Negra, M., Di Maria, R., Everaerts, P., Hall, G., Iles, G., James, T., Komm, M., Laner, C., Lyons, L., Magnan, A.-M., Malik, S., Martelli, A., Milosevic, V., Nash, J., Palladino, V., Pesaresi, M., Raymond, D. M., Richards, A., Rose, A., Scott, E., Seez, C., Shtipliyski, A., Stoye, M., Strebler, T., Summers, S., Tapper, A., Uchida, K., Virdee, T., Wardle, N., Winterbottom, D., Wright, J., Zecchinelli, A. G., Zenz, S. C., Cole, J. E., Hobson, P. R., Khan, A., Kyberd, P., Mackay, C. K., Morton, A., Reid, I. D., Teodorescu, L., Zahid, S., Call, K., Dittmann, J., Hatakeyama, K., Madrid, C., Mcmaster, B., Pastika, N., Smith, C., Bartek, R., Dominguez, A., Uniyal, R., Buccilli, A., Cooper, S. I., Henderson, C., Rumerio, P., West, C., Arcaro, D., Bose, T., Demiragli, Z., Gastler, D., Girgis, S., Pinna, D., Richardson, C., Rohlf, J., Sperka, D., Suarez, I., Sulak, L, Zou, D., Benelli, G., Burkle, B., Coubez, X., Cutts, D., Hadley, M., Hakala, J., Heintz, U., Hogan, J. M., Kwok, K. H. M., Laird, E., Landsberg, G., Lee, J.,Mao, Z., Narain, M., Sagir, S., Syarif, R., Usai, E., Yu, D., Band, R., Brainerd, C., Breedon, R., Calderon De La Barca Sanchez, M., Chertok, M., Conway, J., Conway, R., Cox, P. T., Erbacher, R., Flores, C., Funk, G., Jensen, F., Ko, W., Kukral, O., Lander, R., Mulhearn, M., Pellett, D., Pilot, J., Shi, M., Stolp, D., Taylor, D., Tos, K., Tripathi, M., Wang, Z., Zhang, F., Bachtis, M., Bravo, C., Cousins, R., Dasgupta, A., Florent, A., Hauser, J., Ignatenko, M., Mccoll, N., Regnard, S., Saltzberg, D., Schnaible, C., Valuev, V., Burt, K., Clare, R., Gary, J. W., Shiasi Shirazi, S. M. A., Hanson, G., Karapostoli, G., Kennedy, E., Long, O. R., Olmedo Negrete, M., Paneva, M. I., Si, W., Wang, L., Wei, H., Wimpenny, S., Yates, B. R., Zhang, Y., Branson, J. G., Chang, P., Cittolin, S., Derdzinski, M., Gerosa, R., Gilbert, D., Hashemi, B., Klein, D., Krutelyov, V., Letts, J., Masciovecchio, M., May, S., Padhi, S., Pieri, M., Sharma, V., Tadel, M., Würthwein, F., Yagil, A., Zevi Della Porta, G., Amin, N., Bhandari, R., Campagnari, C., Citron, M., Dutta, V., Franco Sevilla, M., Gouskos, L., Incandela, J., Marsh, B., Mei, H., Ovcharova, A., Qu, H., RIchman, J., Sarica, U., Stuart, D., Wang, S., Yoo, J., Anderson, D., Bornheim, A., Lawhorn, J. M., Lu, N., Newman, H. B., Nguyen, T. Q., Pata, J., Spiropulu, M., Vlimant, J. R., Xie, S., Zhang, Z., Zhu, R. Y., Andrews, M. B., Ferguson, T., Mudholkar, T., Paulini, M., Sun, M., Vorobiev, I., Weinberg, M., Cumalat, J. P., Ford, W. T., Johnson, A., MacDonald, E., Mulholland, T., Patel, R., Perloff, A., Stenson, K., Ulmer, K. A., Wagner, S. R., Alexander, J., Chaves, J., Cheng, Y., Chu, J., Datta, A., Frakenthal, A., Mcdermott, K., Mirman, N., Patterson, J. R., Quach, D., Rinkevicius, A., Ryd, A., Tan, S. M., Tao, Z., Thom, J., Wittich, P., Zientek, M., Abdullin, S., Albrow, M., Alyari, M., Apollinari, G., Apresyan, A., Apyan, A., Banerjee, S., Bauerdick, L. A. T., Beretvas, A., Berryhill, J., Bhat, P. C., Burkett, K., Butler, J. N., Canepa, A., Cerati, G. B., Cheung, H. W. K., Chlebana, F., Cremonesi, M., Duarte, J., Elvira, V. D., Freeman, J., Gecse, Z., Gottschalk, E., Gray, L., Green, D., Grünendahl, S., Gutsche, O., Hall, A.-R., Hanlon, J., Harris, R. M., Hasegawa, S., Heller, R., Hirschauer, J., Hu, Z., Jayatilaka, B., Jindariani, S., Johnson, M., Joshi, U., Klima, B., Kortelainen, M. J., Kreis, B., Lammel, S., Lewis, J., Lincoln, D., Lipton, R., Liu, M., Liu, T., Lykken, J., Maeshima, K., Marraffino, J. M., Mason, D., McBRidge, P., Merkel, P., Mrenna, S., Nahn, S., O'Dell, V., Papadimitriou, V., Pedro, K., Pena, C., Rakness, G., Ravera, F., Ristori, L., Schneider, B., Sexton-Kennedy, E., Smith, N., Soha, A., Spalding, W. J., Spiegel, L., Stoynev, S., Strait, J., Strobbe, N., Taylor, L., Tkaczyk, S., Tran, N. V., Uplegger, L., Vaandering, E. W., Vernieri, C., Verzocchi, M., Vidal, R., Wang, M., WEber, H. A., Acosta, D., Avery, P., Bortignon, P., Bourilkov, D., Brinkerhoff, A., Cadamuro, L., Carnes, A., Cherepanov, V., Curry, D., Errico, F., Field, R. D., Gleyzer, S. V., Joshi, B. M., Kim, M., Konigsberg, J., Korytov, A., Lo, K. H., Ma, P., Matchev, K., Menendez, N., Mitselmakher, G., Rosenzweig, D., Shi, K., Wang, J., Wang, S., Zuo, X., Joshi, Y. R., Linn, S., Adams, T., Askew, A., Hagopian, S., Hagopian, V., Johnson, K. F., Khurana, R., Kolberg, T., Martinez, G., Perry, T., Prosper, H., Schiber, C., Yohay, R., Baarmand, M. M., Bhopatkar, V., Hohlmann, M., Noonan, D., Rahmani, M., Saunders, M., Yumiceva, F., Adams, M. R., Apanasevich, L., Berry, D., Betts, R. R., Cavanaugh, R., Chen, X., Dittmer, S., Evdokimov, O., Gerber, C. Er., Hangal, D. A., Hofman, D. J., Jung, K., Mills, C., Roy, T., Tonjes, M. B., Varelas, N., Wang, H., Wang, X., Wu, Z., Zhang, J., Alhusseini, M., Bilki, B., Clarida, W., Dilsiz, K., Durgut, S., Gandrajula, R. P., Haytmyradov, M., Khristenko, V., Köseyan, O. K., Merlo, J.-P., Mestvirishvili, A., Moeller, A., Nachtman, J., Ogul, H., Onel, Y., Ozok, F., Penzo, A., Snyder, C., Tiras, E., Wetzel, J., Blumenfeld, B., Cocoros, A., Eminizer, N., Fehling, D., Feng, L., Gritsan, A. V., Hung, W. T., Maksimovic, P., Roskes, J., Swartz, M., Xiao, M., Baldenegro Barrera, C., Baringer, P., Bean, A., Boren, S., Bowen, J., Bylinkin, A., Isidori, T., Khalil, S., King, J., Kropivnitskaya, A., Majumder, D., Mcbrayer, W., Minafra, N., Murray, M., Rogan, C., Royon, C., Sanders, S., Schmitz, E., Tapia Takaki, J. D., Wang, Q., Williams, J., Duric, S., Ivanov, A., Kaadze, K., Kim, D., Maravin, Y., Mendis, D. R., Mitchell, T., Modak, A., Mohammadi, A., Rebassoo, F., Wright, D., Baden, A., Baron, O., Belloni, A., Eno, S. C., Feng, Y., Ferraioli, C., Hadley, N. J., Jabeen, S., Jeng, G. Y., Kellogg, R. G., Kunkle, J., Mignerey, A. C., Nabili, S., Ricci-Tam, F., Seidel, M., Shin, Y. H., Skuja, A., Tonwar, S. C., Wong, K., Abercrombie, D., Allen, B., Baty, A., Bi, R., Brandt, S., Busza, W., Cali, I. A., D'Alfonso, M., Gomez Ceballos, G., Goncharov, M., Harris, P., Hsu, D., Hu, M., Klute, M., Kovalskyi, D., Lee, Y.-J., Luckey, P. D., Maier, B., Marini, A. C., Mcginn, C., Mironov, C., Narayanan, S., Niu, X., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Sumorok, K., Tatar, K., Velicanu, D., Wang, J., Wang, T. W., Wyslouch, B., Benvenuti, A. C., Chatterjee, R. M., Evans, A., Hansen, P., Hiltbrand, J., Kalafut, S., Kubota, Y., Lesko, Z., Mans, J. Rusack, R., Wadud, M. A., Acosta, J. G., Oliversos, S., Avdeeva, E., Bloom, K., Claes, D. R., Frangmeier, C., Finco, L., Golf, F., Gonzalez Suarez, R., Kamalieddin, R., Kravchenko, I., Siado, J. E., Snow, G. R., Stieger, B., Godshalk, A., Harrington, C., Iashcili, I., Kharchilava, A., Mclean, C., Nguyen, D., Parker, A., Rappoccio, S., Roozbahani, B., Alverson, G., Barberis, E., Freer, C., Daddad, Y., Hortiangtham, A., Madigan, G., Morse, D. M., Orimoto, T., Skinnari, L., Tishelman=Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Bhattacharya, S., Bueghly, J., Gunter, T., Hahn, K. A., Odell, N., Schmitt, M. H., Sung, K., Trovato, M., Velasco, M., Bucci, R., Dev, N., Hildreth, M., Hurtado Anampa, K., Jessop, C., Karmgard, D. J., Lannon, K., Li, W., Loukas, N., Marinelli, N., Mcalister, I., Meng, F., Mueller, C., Musienko, Y., Planer, M., Ruchti, R., Siddireddy, P., Smith, G., Taroni, S., Wayne, M., Wightman, A., Wolf, M., Woodard, A., Alimena, J., Bylsma, B., Durkin, L. S., Flowers, S., Francis, B., Hill, C., Ji, W., Lefeld, A., Ling, T. Y., Winer, . L., Cooperstein, S., Dezoort, G., Elmer, P., Hardenbrook, J., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kwan, S., Lange, D., Lucchini, M. T., Luo, J., Marlow, D., Mei, K., Ojalvo, I., Olsen, J., Palmer, C., Piroué, P., Salfeld-Nebgen, J., Stickland, D., Tully, C., Wang, Z., Malik, S., Norberg, S., Barker, A., Barnes, V. E., Das, S., Gutay, L., Jones, M., Jung, A. W., Khatiwada, A., Mahakud, B., Miller, D. H., Negro, G., Neumeister, N., Peng, C. C., Piperov, S., Qiu, H., Schulte, J. F., Sun, J., Wang, F., Xiao, R., Xie, W., Cheng, T., Dolen, J., Parashar, N., Ecklund, K. M., Freed, S., Geurts, F. J. M., Kilpatrick, M., Kumar, A., Li, W., Padley, B. P., Redjimi, R., Roberts, J., Rorie, J., Shi, W., Stahl Leiton, A. G., Tu, Z., Zhang, A., Bodek, A., de Barbaro, P., Demina, R., Duh, Y. T., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Han, J., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Tan, P., Taus, R., Chiarito, B., Chou, J. P., Gershtein, Y., Halkiadakis, E., Hart, A., Heindl, M., Hughes, E., Kaplan, S., Kyriacou, S., Laflotte, I., Lath, A., Montalvo, R., Nash, K., Osherson, M., Saka, H., Salur, S., Schnetzer, S., Sheffield, D., Somalwar, S., Stone, R., Thomas, S., Thomassen, P., Acharya, H., Delannoy, A. G., Heideman, J., Riley, G., Spanier, S., Bouhali, O., Celik, A., Dalchenko, M., De Mattia, M., Delgado, A., Dildick, S., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Luo, S., Marley, D., Mueller, R., Overton, D., Perniè, L., Rathjens, D., Safonov, A., Akchrin, N., Damgov, J., De Guio, F., Kunori, S., Lamichhane, K., Lee, S. W., Mengke, T., Muthumuni, S., Peltola, T., Undleeb, S., Volobouev, I., Wang, Z., Whitbeck, A., Greene, S., Furrola, A., Janjam, R., Johns, W., Maguire, C., Melo, A., Ni, H., Padken, K., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Verweij, M., Arenton, M. W., Barria, P., Cox, B., Cummings, G., Hirosky, R., Joyce, M., Ledovskoy, A., Neu, C., Tannenwald, B., Wang, Y., Wolfe, E., Xia, F., Harr, R., Karchin, P. 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    Zhou, Y., Ji, R., Sun, X., Su, J., Meng, D., Gao, Y., & Shen, C. (2019). Plenty Is Plague: Fine-Grained Learning for Visual Question Answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2956699

    View More

  • TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks

    Xiong, H., Cao, Z., Lu, H., Madec, S., Liu, L., & Shen, C. (2019). TasselNetv2: In-field counting of wheat spikes with context-augmented local regression networks. Plant Methods, 15(1), 1–14. https://doi.org/10.1186/s13007-019-0537-2

    View More

  • Feature-based recursive observer design for homography estimation and its application to image stabilization

    Hua, M. D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2019). Feature-based recursive observer design for homography estimation and its application to image stabilization. Asian Journal of Control, 21(4), 1443–1458. https://doi.org/10.1002/asjc.2012

    View More

  • Revisiting Spatio-Angular Trade-off in Light Field Cameras and Extended Applications in Super-Resolution

    Zhu, H., Guo, M., Li, H., Wang, Q., & Robles-Kelly, A. (2019). Revisiting Spatio-Angular Trade-off in Light Field Cameras and Extended Applications in Super-Resolution. IEEE Transactions on Visualization and Computer Graphics, 1–1. https://doi.org/10.1109/tvcg.2019.2957761

    View More

  • Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene

    Kumar, S., Dai, Y., & Li, H. (2019). Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2955131

    View More

  • Full View Optical Flow Estimation Leveraged From Light Field Superpixel

    Zhu, H., Sun, X., Zhang, Q., Wang, Q., Robles-Kelly, A., Li, H., & You, S. (2019). Full View Optical Flow Estimation Leveraged from Light Field Superpixel. IEEE Transactions on Computational Imaging, 1–1. https://doi.org/10.1109/tci.2019.2897937

    View More

  • Identity-Preserving Face Recovery from Stylized Portraits

    Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Identity-Preserving Face Recovery from Stylized Portraits. International Journal of Computer Vision, 127(6), 863–883. https://doi.org/10.1007/s11263-019-01169-1

    View More

  • Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes

    Yu, X., Fernando, B., Hartley, R., & Porikli, F. (2019). Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2916881

    View More

  • Visual Permutation Learning

    Santa Cruz, R., Fernando, B., Cherian, A., & Gould, S. (2018). Visual Permutation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol. PP). IEEE. https://doi.org/10.1109/TPAMI.2018.2873701

    View More

  • Efficient relaxations for dense CRFs with sparse higher-order potentials

    Joy, T., Desmaison, A., Ajanthan, T., Bunel, R., Salzmann, M., Kohli, P., Torr, P. H. S., & Kumar, M. P. (2019). Efficient relaxations for dense CRFs with sparse higher-order potentials. SIAM Journal on Imaging Sciences, 12(1), 287–318. https://doi.org/10.1137/18M1178104

    View More

  • Asynchronous Spatial Image Convolutions for Event Cameras

    Scheerlinck, C., Barnes, N., & Mahony, R. (2019). Asynchronous Spatial Image Convolutions for Event Cameras. IEEE Robotics and Automation Letters, 4(2), 816–822. https://doi.org/10.1109/LRA.2019.2893427

    View More

  • Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials

    Robinson, N. L., Cottier, T. V., & Kavanagh, D. J. (2019). Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials. Journal of Medical Internet Research, 21(5), e13203. https://doi.org/10.2196/13203

    View More

  • A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes

    Khaliq, A., Ehsan, S., Chen, Z., Milford, M., & McDonald-Maier, K. (2020). A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes. IEEE Transactions on Robotics, 36(2), 561–569. https://doi.org/10.1109/TRO.2019.2956352

    View More

  • Representation Learning on Unit Ball with 3D Roto-translational Equivariance

    Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Representation Learning on Unit Ball with 3D Roto-translational Equivariance. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01278-x

    View More

  • Characterizing Subtle Facial Movements via Riemannian Manifold

    Hong, X., Peng, W., Harandi, M., Zhou, Z., Pietikäinen, M., & Zhao, G. (2019). Characterizing Subtle Facial Movements via Riemannian Manifold. ACM Transactions on Multimedia Computing, Communications, and Applications, 15(3s), 1–24. https://doi.org/10.1145/3342227

    View More

  • Robot Expressive Motions: A Survey of Generation and Evaluation Methods

    Venture, G., & Kulić, D. (2019). Robot Expressive Motions. ACM Transactions on Human-Robot Interaction, 8(4), 1–17. https://doi.org/10.1145/3344286

    View More

  • Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks

    Yu, X., Porikli, F., Fernando, B., & Hartley, R. (2019). Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01254-5

    View More

  • NeuroSLAM: a brain-inspired SLAM system for 3D environments

    Yu, F., Shang, J., Hu, Y., & Milford, M. (2019). NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biological Cybernetics. https://doi.org/10.1007/s00422-019-00806-9

    View More

  • A probabilistic challenge for object detection

    Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443–443. https://doi.org/10.1038/s42256-019-0094-4

    View More

  • Target-Specific Siamese Attention Network for Real-time Object Tracking

    Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

    View More

  • Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing

    Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

    View More

  • RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

    Liu, C., Ding, W., Xia, X., Hu, Y., Zhang, B., Liu, J, Zhuang, B. & Guo, G. (2019). RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs. Retrieved from http://arxiv.org/abs/1908.07748

    View More

  • Multisensory Assisted In-hand Manipulation of Objects with a Dexterous Hand

    Korthals, T., Melnik, A., Leitner, J., & Hesse, M. (n.d.). Multisensory Assisted In-hand Manipulation of Objects with a Dexterous Hand. Retrieved from http://arxiv.org/abs/1612.05424

    View More

  • Curiosity Did Not Kill the Robot

    Doering, M., Liu, P., Glas, D. F., Kanda, T., Kulić, D., & Ishiguro, H. (2019). Curiosity Did Not Kill the Robot. ACM Transactions on Human-Robot Interaction, 8(3), 1–24. https://doi.org/10.1145/3326462

    View More

  • TIMTAM: Tunnel-image texturally accorded mosaic for location refinement of underground vehicles with a single camera

    Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). TIMTAM: Tunnel-Image Texturally Accorded Mosaic for Location Refinement of Underground Vehicles With a Single Camera. IEEE Robotics and Automation Letters, 4(4), 4362–4369.

    View More

  • Robotic manipulation and the role of the task in the metric of success

    Ortenzi, V., Controzzi, M., Cini, F., Leitner, J., Bianchi, M., Roa, M. A., & Corke, P. (2019). Robotic manipulation and the role of the task in the metric of success. Nature Machine Intelligence, 1(8), 340–346. https://doi.org/10.1038/s42256-019-0078-4

    View More

  • Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning

    Nash, W., Powell, C., Drummond, T., & Birbilis, N. (2019). Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning. CORROSION, 3397. https://doi.org/10.5006/3397

    View More

  • Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments

    Zapotezny-Anderson, P., & Lehnert, C. (2019). Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments. IFAC-PapersOnLine, 52(30), 120–125. https://doi.org/10.1016/j.ifacol.2019.12.508

    View More

  • Deep Point-to-Subspace Metric Learning for Sketch-Based 3D Shape Retrieval

    Lei, Y., Zhou, Z., Zhang, P., Guo, Y., Ma, Z., & Liu, L. (2019). Deep point-to-subspace metric learning for sketch-based 3D shape retrieval. Pattern Recognition, 96, 106981. https://doi.org/10.1016/J.PATCOG.2019.106981

    View More

  • Inverse Optimal Control for Multiphase Cost Functions

    Jin, W., Kulić, D., Lin, JFS,. Mou, S., & Hirche, S. ( 2019). Inverse Optimal Control for Multiphase Cost Functions. IEEE Transactions on Robotics, 1387 - 1398. https://doi.org/10.1109/TRO.2019.2926388

    View More

  • Using Temporal Information for Recognizing Actions from Still Images

    Herath, S., Fernando, B., & Harandi, M. (2019). Using temporal information for recognizing actions from still images. Pattern Recognition, 96, 106989. https://doi.org/10.1016/J.PATCOG.2019.106989

    View More

  • Immunoregulatory role of exosomes derived from differentiating mesenchymal stromal cells on inflammation and osteogenesis

    Wei, F., Li, Z., Crawford, R., Xiao, Y., & Zhou, Y. (2019). Immunoregulatory role of exosomes derived from differentiating mesenchymal stromal cells on inflammation and osteogenesis. Journal of Tissue Engineering and Regenerative Medicine, term.2947. https://doi.org/10.1002/term.2947

    View More

  • Human Detection Aided by Deeply Learned Semantic Masks

    "X. Wang, C. Shen, H. Li and S. Xu, ""Human Detection Aided by Deeply Learned Semantic Masks,"" in IEEE Transactions on Circuits and Systems for Video Technology.
    doi: 10.1109/TCSVT.2019.2924912"

    View More

  • Learning robust, real-time, reactive robotic grasping

    Morrison, D., Corke, P., & Leitner, J. (2019). Learning robust, real-time, reactive robotic grasping. The International Journal of Robotics Research, 027836491985906. https://doi.org/10.1177/0278364919859066

    View More

  • Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration

    Jo Lane, Rachel A. Robbins, Emilie M. F. Rohan, Kate Crookes, Rohan W. Essex, Ted Maddess, Faran Sabeti, Jamie-Lee Mazlin, Jessica Irons, Tamara Gradden, Amy Dawel, Nick Barnes, Xuming He, Michael Smithson, Elinor McKone; Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration. Journal of Vision 2019;19(6):18. doi: https://doi.org/10.1167/19.6.18.

    View More

  • Practical optimal registration of terrestrial LiDAR scan pairs

    Cai, Z., Chin, T.-J., Bustos, A. P., & Schindler, K. (2019). Practical optimal registration of terrestrial LiDAR scan pairs. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 118–131. https://doi.org/10.1016/j.isprsjprs.2018.11.016

    View More

  • Accelerated Guided Sampling for Multistructure Model Fitting

    Lai, T., Wang, H., Yan, Y., Chin, T.-J., Zheng, J., & Li, B. (2019). Accelerated Guided Sampling for Multistructure Model Fitting. IEEE Transactions on Cybernetics, 1–14. https://doi.org/10.1109/tcyb.2018.2889908

    View More

  • RefineNet: Multi-Path Refinement Networks for Dense Prediction

    Lin, G., Liu, F., Milan, A., Shen, C., & Reid, I. (2019). RefineNet: Multi-Path Refinement Networks for Dense Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2893630

    View More

  • Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss

    Zhang, P., Liu, W., Lu, H., & Shen, C. (2019). Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss. IEEE Transactions on Image Processing, 28(6), 3048–3060. https://doi.org/10.1109/TIP.2019.2893535

    View More

  • Attention Residual Learning for Skin Lesion Classification

    Zhang, J., Xie, Y., Xia, Y., & Shen, C. (2019). Attention Residual Learning for Skin Lesion Classification. IEEE Transactions on Medical Imaging, 38(9), 2092–2103. https://doi.org/10.1109/TMI.2019.2893944

    View More

  • Cardiovascular Diseases

    Verjans J., Veldhuis W.B., Carneiro G., Wolterink J.M., Išgum I., Leiner T. (2019) Cardiovascular Diseases. In: Ranschaert E., Morozov S., Algra P. (eds) Artificial Intelligence in Medical Imaging. Springer, Cham

    View More

  • Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector

    Hunter, J., Jateff, E., & van den Hengel, A. (2019). Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector. In Coastal Research Library (Vol. 31, pp. 89–101). https://doi.org/10.1007/978-3-030-03635-5_6

    View More

  • Robust foreground segmentation and image registration for optical detection of GEO objects

    Do, H. N., Chin, T. J., Moretti, N., Jah, M. K., & Tetlow, M. (2019). Robust foreground segmentation and image registration for optical detection of GEO objects. Advances in Space Research, 64(3), 733–746. https://doi.org/10.1016/j.asr.2019.03.008

    View More

  • Accurate Imagery Recovery Using a Multi-Observation Patch Model

    Zhang, L., Wei, W., Shi, Q., Shen, C., van den Hengel, A., & Zhang, Y. (2019). Accurate imagery recovery using a multi-observation patch model. Information Sciences, 501, 724–741. https://doi.org/10.1016/j.ins.2019.03.033

    View More

  • Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

    Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. IEEE Transactions on Multimedia, 21(11), 2726–2737. https://doi.org/10.1109/TMM.2019.2908352

    View More

  • Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

    Huang, W., Fan, L., Harandi, M., Ma, L., Liu, H., Liu, W., & Gan, C. (2019). Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks. IEEE Transactions on Image Processing, 28(4), 1773–1782. https://doi.org/10.1109/TIP.2018.2877936

    View More

  • Online near time-optimal trajectory planning for industrial robots

    Kim, J., & Croft, E. A. (2019). Online near time-optimal trajectory planning for industrial robots. Robotics and Computer-Integrated Manufacturing, 58, 158–171. https://doi.org/10.1016/J.RCIM.2019.02.009

    View More

  • Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM

    Hall, W., Schmader, T., Aday, A., & Croft, E. (n.d.). Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM. https://doi.org/10.1177/1948550618772582

    View More

  • Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations

    Radmard, S., Moon, A. & Croft, E.A. Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations. Int J of Soc Robotics 11, 105–121 (2019). https://doi.org/10.1007/s12369-018-0480-9

    View More

  • An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers

    Chan, W. P., Pan, M. K. X. J., Croft, E. A., & Inaba, M. (2019). An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers. International Journal of Social Robotics, 1–20. https://doi.org/10.1007/s12369-019-00546-7

    View More

  • Stable Gaussian process based tracking control of Euler–Lagrange systems

    Beckers, T., Kulić, D., & Hirche, S. (2019). Stable Gaussian process based tracking control of Euler–Lagrange systems. Automatica, 103, 390–397. https://doi.org/10.1016/J.AUTOMATICA.2019.01.023

    View More

  • Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment

    Zhou, Y., Li, H., & Kneip, L. (2019). Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment. In IEEE Transactions on Robotics (Vol. 35, pp. 184–199). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TRO.2018.2875382

    View More

  • Adversarial spatio-temporal learning for video deblurring

    Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., & Li, H. (2019). Adversarial spatio-temporal learning for video deblurring. IEEE Transactions on Image Processing, 28(1), 291–301. https://doi.org/10.1109/TIP.2018.2867733

    View More

  • Picking the right robotics challenge

    Leitner, J. (2019). Picking the right robotics challenge. Nature Machine Intelligence, 1(3), 162–162. https://doi.org/10.1038/s42256-019-0031-6

    View More

  • Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy

    Marmol, A., Banach, A., & Peynot, T. (2019). Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy. IEEE Robotics and Automation Letters, 4(2), 918–925. https://doi.org/10.1109/LRA.2019.2892199

    View More

  • Modular field robot deployment for inspection of dilapidated buildings

    Cordie, T. P., Bandyopadhyay, T., Roberts, J., Dunbabin, M., Greenop, K., Dungavell, R., & Steindl, R. (2019). Modular field robot deployment for inspection of dilapidated buildings. Journal of Field Robotics, rob.21872. https://doi.org/10.1002/rob.21872

    View More

  • On the choice of grasp type and location when handing over an object

    Cini, F., Ortenzi, V., Corke, P., & Controzzi, M. (2019). On the choice of grasp type and location when handing over an object. Science Robotics, 4(27), eaau9757. https://doi.org/10.1126/scirobotics.aau9757

    View More

  • Learning to Fuse Multiscale Features for Visual Place Recognition

    Mao, J., Hu, X., He, X., Zhang, L., Wu, L., & Milford, M. J. (2019). Learning to Fuse Multiscale Features for Visual Place Recognition. IEEE Access, 7, 5723–5735. https://doi.org/10.1109/ACCESS.2018.2889030

    View More

  • Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

    Hausler, S., Jacobson, A., & Milford, M. (2019). Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods. IEEE Robotics and Automation Letters, 4(2), 1924–1931. https://doi.org/10.1109/LRA.2019.2898427

    View More

  • Semantic–geometric visual place recognition: a new perspective for reconciling opposing views

    Garg, S., Suenderhauf, N., & Milford, M. (2019). Semantic–geometric visual place recognition: a new perspective for reconciling opposing views. The International Journal of Robotics Research, 027836491983976. https://doi.org/10.1177/0278364919839761

    View More

  • Adaptive Importance Learning for Improving Lightweight Image Super-resolution Network

    Zhang, L., Wang, P., Shen, C., Liu, L., Wei, W., Zhang, Y., & van den Hengel, A. (2020). Adaptive Importance Learning for Improving Lightweight Image Super-Resolution Network. International Journal of Computer Vision, 128(2), 479–499. https://doi.org/10.1007/s11263-019-01253-6

    View More

  • Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study

    Yan, Y., Tan, M., Tsang, I. W., Yang, Y., Shi, Q., & Zhang, C. (2020). Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study. IEEE Transactions on Knowledge and Data Engineering, 32(2), 288–301. https://doi.org/10.1109/TKDE.2018.2882197

    View More

  • One-step adaptive markov random field for structured compressive sensing

    Suwichaya Suwanwimolkul, Lei Zhang, Damith C. Ranasinghe, Qinfeng Shi, One-step adaptive markov random field for structured compressive sensing, Signal Processing, Volume 156, 2019,Pages 116-144, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2018.10.020.

    View More

  • Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI

    Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Pre and post-hoc diagnosis and interpretation of malignancy from breast DCE-MRI. Medical Image Analysis, 58, 101562. https://doi.org/10.1016/j.media.2019.101562

    View More

  • On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC

    Tran, N.-T., Le Tan, D.-K., Doan, A.-D., Do, T.-T., Bui, T.-A., Tan, M., & Cheung, N.-M. (2019). On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC. IEEE Transactions on Image Processing, 28(4), 1675–1690. http://doi.org/10.1109/TIP.2018.2881829

    View More

  • Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation

    Zhang, T., Lin, G., Cai, J., Shen, T., Shen, C., & Kot, A. C. (2019). Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation. IEEE Transactions on Multimedia, 21(11), 2930–2941. https://doi.org/10.1109/TMM.2019.2914870

    View More

  • Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

    Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2019). Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion. IEEE Robotics and Automation Letters, 4(2), 177–184. http://doi.org/10.1109/LRA.2018.2884765

    View More

  • Second-order Temporal Pooling for Action Recognition

    Cherian, A., Gould, S. Second-order Temporal Pooling for Action Recognition. Int J Comput Vis 127, 340–362 (2019). https://doi.org/10.1007/s11263-018-1111-5

    View More

  • Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

    Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119–133. https://doi.org/10.1016/j.patcog.2019.01.006

    View More

  • Memory Efficient Max Flow for Multi-label Submodular MRFs

    Ajanthan, T., Hartley, R., & Salzmann, M. (2019). Memory Efficient Max Flow for Multi-Label Submodular MRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(4), 886–900. https://doi.org/10.1109/TPAMI.2018.2819675

    View More

Conference Papers

  • Self-supervised learning for single view depth and surface normal estimation

    Zhan, H., Weerasekera, C. S., Garg, R., & Reid, I. (2019). Self-supervised learning for single view depth and surface normal estimation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 4811–4817. https://doi.org/10.1109/ICRA.2019.8793984

    View More

  • Attention-guided network for ghost-free high dynamic range imaging

    Yan, Q., Gong, D., Shi, Q., Van Den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided network for ghost-free high dynamic range imaging. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1751–1760. https://doi.org/10.1109/CVPR.2019.00185

    View More

  • Bayesian Generative Active Deep Learning

    Tran, T., Do, T.-T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. Retrieved from https://arxiv.org/pdf/1904.11643

    View More

  • Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

    Purkait, P., Zach, C., & Reid, I. (2019). Seeing behind Things: Extending Semantic Segmentation to Occluded Regions. IEEE International Conference on Intelligent Robots and Systems, 1998–2005. https://doi.org/10.1109/IROS40897.2019.8967582

    View More

  • NeuRoRA: Neural Robust Rotation Averaging

    Purkait P., Chin TJ., Reid I. (2020) NeuRoRA: Neural Robust Rotation Averaging. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12369. Springer, Cham. https://doi.org/10.1007/978-3-030-58586-0_9

    View More

  • Below horizon aircraft detection using deep learning for vision-based sense and avoid

    James, J., Ford, J. J., & Molloy, T. L. (2019). Below horizon aircraft detection using deep learning for vision-based sense and avoid. 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019, 965–970. https://doi.org/10.1109/ICUAS.2019.8798096

    View More

  • Filter Early, Match Late: Improving Network-Based Visual Place Recognition

    Hausler, S., Jacobson, A., & Milford, M. (2019). Filter Early, Match Late: Improving Network-Based Visual Place Recognition. IEEE International Conference on Intelligent Robots and Systems, 3268–3275. https://doi.org/10.1109/IROS40897.2019.8967783

    View More

  • Visual place recognition for aerial robotics: Exploring accuracy-computation trade-off for local image descriptors

    Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M., & McDonald-Maier, K. D. (2019). Visual place recognition for aerial robotics: Exploring accuracy-computation trade-off for local image descriptors. Proceedings - 2019 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2019, 103–108. https://doi.org/10.1109/AHS.2019.00011

    View More

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    Abedin, A., Hamid Rezatofighi, S., Shi, Q., & Ranasinghe, D. C. (2019). Sparsesense: Human activity recognition from highly sparse sensor data-streams using set-based neural networks. IJCAI International Joint Conference on Artificial Intelligence, 2019-August, 5780–5786. https://doi.org/10.24963/ijcai.2019/801

    View More

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    Li R., Xian K., Shen C., Cao Z., Lu H., Hang L. (2019) Deep Attention-Based Classification Network for Robust Depth Prediction. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_41

    View More

  • Sim-to-real transfer of robot learning with variable length inputs

    Dasagi, V., Lee, R., Mou, S., Bruce, J., Sünderhauf, N., & Leitner, J. (2018). Sim-to-Real Transfer of Robot Learning with Variable Length Inputs. Australasian Conference on Robotics and Automation, ACRA, 2019-December. Retrieved from http://arxiv.org/abs/1809.07480

    View More

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    Ng, Y., Van Goor, P., Mahony, R., & Hamel, T. (2019). Attitude Observation for Second Order Attitude Kinematics. Proceedings of the IEEE Conference on Decision and Control, 2019-December, 2536–2542. https://doi.org/10.1109/CDC40024.2019.9029785

    View More

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    Mahony, R. (2019). A novel passivity-based trajectory tracking control for conservative mechanical systems. Proceedings of the IEEE Conference on Decision and Control, 2019-December, 4259–4266. https://doi.org/10.1109/CDC40024.2019.9029222

    View More

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    Camps, S.M., Houben, T., Carneiro, G., Edwards, C., Antico, M., Dunnhofer, M., Martens, E.G.H.J., Baeza, J.A., Vanneste, B.G.L., van Limbergen, E.J., de With, P.H.N., Verhaegen, F., & Fontanarosa, D. (2019) Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy. In ASMIRT / AACRT 2019 Conference, 28-31 March 2019, Adelaide, S.A

    View More

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    Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Spectral-GANs for High-Resolution 3D Point-cloud Generation. Retrieved from http://arxiv.org/abs/1912.01800

    View More

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    Korthals, T., Rudolph, D., Leitner, J., Hesse, M., & Ruckert, U. (2019). Multi-modal generative models for learning epistemic active sensing. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 3319–3325. https://doi.org/10.1109/ICRA.2019.8794458

    View More

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    Abbasnejad, E., Wu, Q., Shi, Q., & Van Den Hengel, A. (2019). What’s to know? uncertainty as a guide to asking goal-oriented questions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4150–4159. https://doi.org/10.1109/CVPR.2019.00428

    View More

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    Zhang, X., Cao, J., Shen, C., & You, M. (2019). Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification *.

    View More

  • Real-time Tracker with Fast Recovery from Target Loss

    Bay, A., Sidiropoulos, P., Vazquez, E., & Sasdelli, M. (2019). Real-time Tracker with Fast Recovery from Target Loss. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2019-May, 1932–1936. https://doi.org/10.1109/ICASSP.2019.8682171

    View More

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    Kosaraju, V., Sadeghian, A., Martín-Martín, R., Reid, I., Rezatofighi, S. H., & Savarese, S. (2019). Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks.

    View More

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    Mondal, R., Purkait, P., Santra, S., & Chanda, B. (2019). Morphological networks for image de-raining. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11414 LNCS, 262–275. https://doi.org/10.1007/978-3-030-14085-4_21

    View More

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    Zhang, P., Liu, W., Lei, Y., Lu, H., & Yang, X. (2019). Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion.

    View More

  • Visual Localization under Appearance Change: A Filtering Approach

    Doan, A. D., Latif, Y., Chin, T. J., Liu, Y., Ch’Ng, S. F., Do, T. T., & Reid, I. (2019). Visual Localization under Appearance Change: A Filtering Approach. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945810

    View More

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    Ch’ng, S. F., Khosravian, A., Doan, A. D., & Chin, T. J. (2019). Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion. IEEE International Conference on Intelligent Robots and Systems, 7489–7496. https://doi.org/10.1109/IROS40897.2019.8967643

    View More

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    Cai, Z., & Koltun, V. (2019). Consensus Maximization Tree Search Revisited. Retrieved from https://github.

    View More

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    Gale, W., Oakden-Rayner, L., Carneiro, G., Palmer, L. J., & Bradley, A. P. (2019). Producing radiologist-quality reports for interpretable deep learning. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 1275–1279. https://doi.org/10.1109/ISBI.2019.8759236

    View More

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    Tian, Y., Pu, L. Z. C. T., Singh, R., Burt, A. D., & Carneiro, G. (2019). One-stage five-class polyp detection and classification. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 70–73. https://doi.org/10.1109/ISBI.2019.8759521

    View More

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    Snaauw, G., Gong, D., Maicas, G., Hengel, A. Van Den, Niessen, W. J., Verjans, J., & Carneiro, G. (2019). End-to-end diagnosis and segmentation learning from cardiac magnetic resonance imaging. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 802–805. https://doi.org/10.1109/ISBI.2019.8759276

    View More

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    Maicas, G., Snaauw, G., Bradley, A. P., Reid, I., & Carneiro, G. (2019). Model agnostic saliency for weakly supervised lesion detection from breast DCE-MRI. Proceedings - International Symposium on Biomedical Imaging, 2019-April, 1057–1060. https://doi.org/10.1109/ISBI.2019.8759402

    View More

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    Collins, J., Howard, D., & Leitner, J. (2019). Quantifying the reality gap in robotic manipulation tasks. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 6706–6712. https://doi.org/10.1109/ICRA.2019.8793591

    View More

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    Rowntree, T., Pontecorvo, C., & Reid, I. (2019, December 1). Real-Time Human Gaze Estimation. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945919

    View More

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    Tian, Y., Yu, X., Fan, B. †, Wu, F., Heijnen, H., & Balntas, V. (2019). SOSNet: Second Order Similarity Regularization for Local Descriptor Learning.

    View More

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    Ng, K. W., Mahony, R., & Lau, D. (2019). A dual joystick-trackball interface for accurate and time-efficient teleoperation of cable-driven parallel robots within large workspaces. In Mechanisms and Machine Science (Vol. 74, pp. 391–402). https://doi.org/10.1007/978-3-030-20751-9_33

    View More

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    Liu, L., Li, H., & Dai, Y. (2019). Stochastic attraction-repulsion embedding for large scale image localization. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 2570–2579. https://doi.org/10.1109/ICCV.2019.00266

    View More

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    Zhang, K., Luo, W., Ma, L., Liu, W., & Li, H. (2019). Learning Joint Gait Representation via Quintuplet Loss Minimization.

    View More

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    Zhang, H., Dai, Y., Li, H., & Koniusz, P. (2019). Deep Stacked Hierarchical Multi-patch Network for Image Deblurring.

    View More

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    Zhong, Y., Ji, P., Wang, J., Dai, Y., & Li, H. (2019). Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes.

    View More

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    Cheng, X., Zhong, Y., Dai, Y., Ji, P., & Li, H. (2019). Noise-aware unsupervised deep lidar-stereo fusion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6332–6341. https://doi.org/10.1109/CVPR.2019.00650

    View More

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    Pan, L., Scheerlinck, C., Yu, X., Hartley, R., Liu, M., & Dai, Y. (2019). Bringing a blurry frame alive at high frame-rate with an event camera. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6813–6822. https://doi.org/10.1109/CVPR.2019.00698

    View More

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    Pan, L., Hartley, R., Liu, M., & Dai, Y. (2019). Phase-only image based kernel estimation for single image blind deblurring. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6027–6036. https://doi.org/10.1109/CVPR.2019.00619

    View More

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    Campbell, D., Petersson, L., Kneip, L., Li, H., & Gould, S. (2019). The alignment of the spheres: Globally-optimal spherical mixture alignment for camera pose estimation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 11788–11798. https://doi.org/10.1109/CVPR.2019.01207

    View More

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    De Bem, R., Ghosh, A., Boukhayma, A., Ajanthan, T., Siddharth, N., & Torr, P. (2019). A conditional deep generative model of people in natural images. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 1449–1458. https://doi.org/10.1109/WACV.2019.00159

    View More

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    Zhuang, Z., Leitner, J., & Mahony, R. (2019). Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4752–4759. https://doi.org/10.1109/IROS40897.2019.8968136

    View More

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    Mahony, R., Kennedy, G., Mahony, R., Kennedy, G., & Ila, V. (n.d.). A Perception Pipeline for Robotic Harvesting of Green Asparagus. IFAC PapersOnLine, 52(30), 288–293. https://doi.org/10.1016/j.ifacol.2019.12.536

    View More

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    Qiu, S., Anwar, S., & Barnes, N. (2019). Geometric Feedback Network for Point Cloud Classification. Retrieved from http://arxiv.org/abs/1911.12885

    View More

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    Nekrasov, V., Dharmasiri, T., Spek, A., Drummond, T., Shen, C., & Reid, I. (2019). Real-time joint semantic segmentation and depth estimation using asymmetric annotations. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 7101–7107. https://doi.org/10.1109/ICRA.2019.8794220

    View More

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    Robinson, N. L., Turkay, S., Cooper, L. A. N., & Johnson, D. (2019, December 2). Social Robots with Gamification Principles to Increase Long-Term User Interaction. 359–363. https://doi.org/10.1145/3369457.3369494

    View More

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    Halodova, L., Dvorrakova, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajnik, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7033–7039. https://doi.org/10.1109/IROS40897.2019.8967994

    View More

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    Pond, L., Hojnik, T., Flick, P., & Roberts, J. (2019). Control comparison and evaluation of pneumatic and electric linear actuators for configurable center-hub wheels. Proceedings of the Australasian Conference on Robotics and Automation 2019:. Australian Robotics and Automation Association (ARAA), Australia, pp. 1-8.

    View More

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    Chin, T. J., Bagchi, S., Eriksson, A., & Van Schaik, A. (2019). Star tracking using an event camera. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 1646–1655. https://doi.org/10.1109/CVPRW.2019.00208

    View More

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    Salahat, E., Asselineau, C. A., Coventry, J., & Mahony, R. (2019). Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms. IECON Proceedings (Industrial Electronics Conference), 2019-October, 763–769. https://doi.org/10.1109/IECON.2019.8927123

    View More

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    Bruggemann, T. S., Ford, J. J., White, G., & Perez, T. (2019). Bushfire emergency response simulation.

    View More

  • Bushfire emergency response uncertainty quantification

    Bruggemann, T., Ford, J. J., White, G., Perez, T., & Power, W. (2019). Bushfire emergency response uncertainty quantification. Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM2019). The Modelling and Simulation Society of Australia and New Zealand Inc., Australia, pp. 42-48.

    View More

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    Cao, J., Mo, L., Zhang, Y., Jia, K., Shen, C., & Tan, M. (2019). Multi-marginal Wasserstein GAN.

    View More

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    Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., & Hancock, E. (2019). Deep hashing by discriminating hard examples. MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 1535–1542. https://doi.org/10.1145/3343031.3350927

    View More

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    Wang, Z., Liu, T., Shi, Q., Pawan Kumar, M., & Zhang, J. (2019). New Convex Relaxations for MRF Inference with Unknown Graphs.

    View More

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    Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., & Hartley, R. (2019). Learning to find common objects across few image collections. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5116–5125. https://doi.org/10.1109/ICCV.2019.00522

    View More

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    Fang, P., Zhou, J., Roy, S., Petersson, L., & Harandi, M. (2019). Bilinear attention networks for person retrieval. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8029–8038. https://doi.org/10.1109/ICCV.2019.00812

    View More

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    Kumar Roy, S., Harandi, M., Nock, R., & Hartley, R. (n.d.). Siamese Networks: The Tale of Two Manifolds. Retrieved from https://github.com/sumo8291/

    View More

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    Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Predicting the future: A jointly learnt model for action anticipation. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5561–5570. https://doi.org/10.1109/ICCV.2019.00566

    View More

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    Le, H., Xu, M., Hoang, T., & Milford, M. (2019). Hierarchical encoding of sequential data with compact and sub-linear storage cost. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9823–9832. https://doi.org/10.1109/ICCV.2019.00992

    View More

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    Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., Van Den Hengel, A., & Zhu, W. (2019). Watch, reason and code: Learning to represent videos using program. MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, 1543–1551. https://doi.org/10.1145/3343031.3351094

    View More

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    Cai, M., Zhan, H., Weerasekera, C. S., Li, K., & Reid, I. (2019). Camera relocalization by exploiting multi-view constraints for scene coordinates regression. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 3769–3777. https://doi.org/10.1109/ICCVW.2019.00469

    View More

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    Li, L., Khan, S., & Barnes, N. (2019). Silhouette-assisted 3D object instance reconstruction from a cluttered scene. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2080–2088. https://doi.org/10.1109/ICCVW.2019.00263

    View More

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    Mao, W., Liu, M., Salzmann, M., & Li, H. (2019). Learning trajectory dependencies for human motion prediction. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9488–9496. https://doi.org/10.1109/ICCV.2019.00958

    View More

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    Gupta, K., Petersson, L., & Hartley, R. (2019). CullNet: Calibrated and pose aware confidence scores for object pose estimation. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2758–2766. https://doi.org/10.1109/ICCVW.2019.00337

    View More

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    Yu, X., Tian, Y., Porikli, F., Hartley, R., Li, H., Heijnen, H., & Balntas, V. (2019). Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss.

    View More

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    Khan, S. H., Guo, Y., Hayat, M., & Barnes, N. (2019). Unsupervised primitive discovery for improved 3D generative modeling. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9731–9740. https://doi.org/10.1109/CVPR.2019.00997

    View More

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    Xie, Y., Lu, H., Zhang, J., Shen, C., & Xia, Y. (2019). Deep Segmentation-Emendation Model for Gland Instance Segmentation. https://doi.org/10.1007/978-3-030-32239-7_52

    View More

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    Wang, X., Yin, W., Kong, T., Jiang, Y., Li, L., & Shen, C. (2019). Task-Aware Monocular Depth Estimation for 3D Object Detection. Retrieved from http://arxiv.org/abs/1909.07701

    View More

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    Xing, Y., Ge, Z., Zeng, R., Mahapatra, D., Seah, J., Law, M., & Drummond, T. (2019). Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation. https://doi.org/10.1007/978-3-030-32226-7_84

    View More

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    Avraham, G., Zuo, Y., & Drummond, T. (2019). Parallel optimal transport gan. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4406–4415. https://doi.org/10.1109/CVPR.2019.00454

    View More

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    Chen, B., Cao, J., Parra, A., & Chin, T. J. (2019). Satellite pose estimation with deep landmark regression and nonlinear pose refinement. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2816–2824. https://doi.org/10.1109/ICCVW.2019.00343

    View More

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    Xiong, H., Lu, H., Liu, C., Liu, L., Cao, Z., & Shen, C. (2019). From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. Retrieved from https://github.

    View More

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    Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., & Shen, C. (2019). Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network.

    View More

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    Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., & Yan, Y. (2019). Exploiting temporal consistency for real-time video depth estimation *. Retrieved from https://tinyurl.com/STCLSTM

    View More

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    Sarker, C., Mejias, L., Maire, F., & Woodley, A. (2020). Evaluation of the Impact of Image Spatial Resolution in Designing a Context-Based Fully Convolution Neural Networks for Flood Mapping. 1–8. https://doi.org/10.1109/dicta47822.2019.8945888

    View More

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    Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. Retrieved from http://arxiv.org/abs/1908.04930

    View More

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    Bian, J.-W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.-M., & Reid, I. (2019). Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video.

    View More

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    Li, X., Ma, C., Wu, B., He, Z., & Yang, M.-H. (2019). Target-Aware Deep Tracking.

    View More

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    Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. 1–8. https://doi.org/10.1109/dicta47822.2019.8945949

    View More

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    Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Indices matter: Learning to index for deep image matting. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 3265–3274. https://doi.org/10.1109/ICCV.2019.00336

    View More

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    Yin, W., Liu, Y., Shen, C., & Yan, Y. (2019). Enforcing geometric constraints of virtual normal for depth prediction. Retrieved from https://tinyurl.com/

    View More

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    Doan, D., Latif, Y., Chin, T. J., Liu, Y., Do, T. T., & Reid, I. (2019). Scalable place recognition under appearance change for autonomous driving. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9318–9327. https://doi.org/10.1109/ICCV.2019.00941

    View More

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    Zheng, Y., Yu, X., Liu, M., & Zhang, S. (2019). YP.ZHENG ET AL: RESIDUAL MULTISCALE BASED SINGLE IMAGE DERAINING Residual Multiscale Based Single Image Deraining.

    View More

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    Ajanthan, T., Dokania, P., Hartley, R., & Torr, P. (2019). Proximal mean-field for neural network quantization. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 4870–4879. https://doi.org/10.1109/ICCV.2019.00497

    View More

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    Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind Your Neighbours: Image Annotation with Metadata Neighbourhood Graph Co-Attention Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2956-2964

    View More

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    Stanislas, L., Nubert, J., Dugas, D., Nitsch, J., Suenderhauf, N., Siegwart, R., Cadena, C., & Peynot, T. (2019). Airborne particle classification in LiDAR point clouds using deep learning. Proceedings of the 12th Conference on Field and Service Robotics:Keio University, Japan, pp. 1-14.

    View More

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    Du, Y., Hetherington, N. J., Oon, C. L., Chan, W. P., Quintero, C. P., Croft, E., & MacHiel Van Der Loos, H. F. (2019). Group surfing: A pedestrian-based approach to sidewalk robot navigation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 6518–6524. https://doi.org/10.1109/ICRA.2019.8793608

    View More

  • Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

    Shi, Y., Liu, L., Yu, X., & Li, H. (2019). Spatial-Aware Feature Aggregation for Cross-View Image based Geo-Localization.

    View More

  • Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse

    Zhang, K., Luo, W., Ma, L., & Li, H. (2019). Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse. In AAAI 2019 (pp. 9203–9210). Retrieved from www.aaai.org

    View More

  • Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects

    Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2019). Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects. Retrieved from http://arxiv.org/abs/1907.06371

    View More

  • Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality

    Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2019). Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2019.2930051

    View More

  • An Evaluation of Feature Matchers for Fundamental Matrix Estimation

    Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., & Reid, I. (n.d.). An Evaluation of Feature Matchers for Fundamental Matrix Estimation. Retrieved from https://jwbian.net/Papers/FM_BMVC19.pdf

    View More

  • Deep Anomaly Detection with Deviation Networks

    Pang, G., Shen, C., & Van Den Hengel, A. (2019). Deep anomaly detection with deviation networks. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 353–362. https://doi.org/10.1145/3292500.3330871

    View More

  • Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation

    Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11954 LNCS, 353–366. https://doi.org/10.1007/978-3-030-36711-4_30

    View More

  • EMPNet: Neural Localisation and Mapping Using Embedded Memory Points

    Avraham, G., Zuo, Y., Dharmasiri, T., & Drummond, T. (2019). EMPNet: Neural localisation and mapping using embedded memory points. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8119–8128. https://doi.org/10.1109/ICCV.2019.00821

    View More

  • Visual Controllers for Relative Positioning in Indoor Settings

    Mejias, L., & Campoy, P. (2019). Visual controllers for relative positioning in indoor settings. 2019 International Conference on Unmanned Aircraft Systems, ICUAS 2019, 1194–1200. https://doi.org/10.1109/ICUAS.2019.8797954

    View More

  • Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management

    Dunbabin, M., Dayoub, F., Lamont, R., & Martin, S. (2019). Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management. Retrieved from http://icra-2019-uwroboticsperception.ge.issia.cnr.it/assets/ICRA19-WS-URP-CameraReadySubmissions/ICRA19-WS-URP-Paper-20

    View More

  • Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes

    Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes. Retrieved from https://arxiv.org/pdf/1906.09433

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  • CVPR19 Tracking and Detection Challenge: How crowded can it get?

    Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., … Taixé, T. (n.d.). CVPR19 Tracking and Detection Challenge: How crowded can it get? Retrieved from http://www.motchallenge.net/

    View More

  • Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators

    Hojnik, T., Flick, P., Bandyopadhyay, T., & Roberts, J. (2019). Dynamic manipulation of gear ratio and ride height for a novel compliant wheel using pneumatic actuators. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 987–992. https://doi.org/10.1109/ICRA.2019.8793681

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  • Benchmarking Sampling-based Probabilistic Object Detectors

    Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (n.d.). Benchmarking Sampling-based Probabilistic Object Detectors. Retrieved from http://openaccess.thecvf.com/content_CVPRW_2019/papers/Uncertainty and Robustness in Deep Visual Learning/Miller_Benchmarking_Sampling-based_Probabilistic_Object_Detectors_CVPRW_2019_paper.pdf

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  • Event Cameras, Contrast Maximization and Reward Functions: An Analysis

    Stoffregen, T., & Kleeman, L. (2019). Event Cameras, Contrast Maximization and Reward Functions: an Analysis.

    View More

  • Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

    Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression.

    View More

  • RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

    Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., & Reid, I. (2019). RGBD based dimensional decomposition residual network for 3D semantic scene completion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 7685–7694. https://doi.org/10.1109/CVPR.2019.00788

    View More

  • Associatively Segmenting Instances and Semantics in Point Clouds

    Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively Segmenting Instances and Semantics in Point Clouds. Retrieved from https://github.com/WXinlong/ASIS.

    View More

  • Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation

    Do, T. T., Hoang, T., Le Tan, D. K., Pham, T., Le, H., Cheung, N. M., & Reid, I. (2019). Binary constrained deep hashing network for image retrieval without manual annotation. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 695–704. https://doi.org/10.1109/WACV.2019.00079

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  • Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

    Yan, Q., Gong, D., Zhang, P., Shi, Q., Sun, J., Reid, I., & Zhang, Y. (2019). Multi-scale dense networks for deep high dynamic range imaging. Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 41–50. https://doi.org/10.1109/WACV.2019.00012

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  • CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

    Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning.

    View More

  • Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

    Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation.

    View More

  • Knowledge Adaptation for Efficient Semantic Segmentation

    He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge adaptation for efficient semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 578–587. https://doi.org/10.1109/CVPR.2019.00067

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  • Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

    Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 1705–1714. https://doi.org/10.1109/ICCV.2019.00179

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  • Actively Seeking and Learning from Live Data

    Teney, D., & Van Den Hengel, A. (2019). Actively Seeking and Learning from Live Data.

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  • FCOS: Fully Convolutional One-Stage Object Detection

    Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection.

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  • A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

    Do, T. T., Tran, T., Reid, I., Kumar, V., Hoang, T., & Carneiro, G. (2019). A theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 10396–10405. https://doi.org/10.1109/CVPR.2019.01065

    View More

  • TopNet: Structural Point Cloud Decoder

    Tchapmi, L. P., Kosaraju, V., Rezatofighi, H., Reid, I., & Savarese, S. (2019). TopNet: Structural Point Cloud Decoder. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html

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  • A Generative Adversarial Density Estimator

    Abbasnejad, M. E., Shi, Q., Van Den Hengel, A., & Liu, L. (2019). A generative adversarial density estimator. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 10774–10783. https://doi.org/10.1109/CVPR.2019.01104

    View More

  • Event-Based Motion Segmentation by Motion Compensation

    Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., & Scaramuzza, D. (2019). Event-Based Motion Segmentation by Motion Compensation. Retrieved from https://youtu.be/0q6ap

    View More

  • CED: Color Event Camera Dataset

    Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., & Scaramuzza, D. (2019). CED: Color Event Camera Dataset.

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  • Min-Max Statistical Alignment for Transfer Learning

    Herath, S., Harandi, M., Fernando, B., & Nock, R. (2019). Min-max statistical alignment for transfer learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9280–9289. https://doi.org/10.1109/CVPR.2019.00951

    View More

  • Expression of Curiosity in Social Robots

    Ceha, J., Chhibber, N., Goh, J., McDonald, C., Oudeyer, P.-Y., Kulić, D., & Law, E. (2019). Expression of Curiosity in Social Robots. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–12. https://doi.org/10.1145/3290605.3300636

    View More

  • Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

    Wilde, N., Kulic, D., & Smith, S. L. (2019). Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions. IEEE Robotics and Automation Letters, 4(2), 1691–1698. https://doi.org/10.1109/LRA.2019.2897342

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  • The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

    Meyer, B. J., & Drummond, T. (2019). The importance of metric learning for robotic vision: Open set recognition and active learning. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2924–2931. https://doi.org/10.1109/ICRA.2019.8794188

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  • Visual SLAM: Why Bundle Adjust?

    Bustos, A. P., Chin, T. J., Eriksson, A., & Reid, I. (2019). Visual SLAM: Why bundle adjust? Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2385–2391. https://doi.org/10.1109/ICRA.2019.8793749

    View More

  • Learning to Adapt for Stereo

    Tonioni, A., Rahnama, O., Joy, T., Stefano, L. Di, Ajanthan, T., & Torr, P. H. S. (2019). Learning to Adapt for Stereo.

    View More

  • Single image deblurring and camera motion estimation with depth map

    Pan, L., Dai, Y., & Liu, M. (2019). Single image deblurring and camera motion estimation with depth map. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 2116–2125). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/WACV.2019.00229

    View More

  • Deep Learning AI for Corrosion Detection

    Nash, W., Drummond, T., & Birbilis, N. (2019, May 15). Deep Learning AI for Corrosion Detection. Retrieved from https://www.onepetro.org/conference-paper/NACE-2019-13267

    View More

  • Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization

    Wu, L., Crawford, R., & Roberts, J. (2019). Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 7360–7366. https://doi.org/10.1109/ICRA.2019.8794384

    View More

  • LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles

    Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). LookUP: Vision-only real-time precise underground localisation for autonomous mining vehicles. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 1444–1450. https://doi.org/10.1109/ICRA.2019.8794453

    View More

  • Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

    Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3748–3753. https://doi.org/10.1109/IROS40897.2019.8968525

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  • Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

    Garg, S., Babu, M. V., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., Drummond, T., & Milford, M. (2019). Look no deeper: Recognizing places from opposing viewpoints under varying scene appearance using single-view depth estimation. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 4916–4923. https://doi.org/10.1109/ICRA.2019.8794178

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  • Recovering Faces From Portraits with Auxiliary Facial Attributes

    *Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Recovering Faces From Portraits with Auxiliary Facial Attributes. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 406–415). Waikoloa Village, Hawaii, United States: IEEE. http://doi.org/10.1109/WACV.2019.00049

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  • ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

    Song, X., Wang, P., Zhou, D., Zhu, R., Guan, C., Dai, Y., Su, H., Li, H., & Yang, R. (2019). APOLLOCAR3D: A large 3D car instance understanding benchmark for autonomous driving. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 5447–5457. https://doi.org/10.1109/CVPR.2019.00560

    View More

  • Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks

    Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & Hengel, A. Van Den. (2019). Neighbourhood watch: Referring expression comprehension via language-guided graph attention networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1960–1968. https://doi.org/10.1109/CVPR.2019.00206

    View More

  • Object Captioning and Retrieval with Natural Language

    Nguyen, A., Tran, Q. D., Do, T. T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2019). Object captioning and retrieval with natural language. Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2584–2592. https://doi.org/10.1109/ICCVW.2019.00316

    View More

  • Visual Question Answering as Reading Comprehension

    Li, H., Wang, P., Shen, C., & Hengel, A. Van Den. (2019). Visual question answering as reading comprehension. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 6312–6321. https://doi.org/10.1109/CVPR.2019.00648

    View More

  • Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation

    Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured binary neural networks for accurate image classification and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 413–422. https://doi.org/10.1109/CVPR.2019.00050

    View More

  • Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells

    Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Fast neural architecture search of compact semantic segmentation models via auxiliary cells. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9118–9127. https://doi.org/10.1109/CVPR.2019.00934

    View More

  • Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter

    Morrison, D., Corke, P., & Leitner, J. (2019). Multi-view picking: Next-best-view reaching for improved grasping in clutter. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 8762–8768. https://doi.org/10.1109/ICRA.2019.8793805

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  • Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection

    Miller, Di., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. Proceedings - IEEE International Conference on Robotics and Automation, 2019-May, 2348–2354. https://doi.org/10.1109/ICRA.2019.8793821

    View More

  • Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition

    Li, H., Wang, P., Shen, C., & Zhang, G. (2019). Show, attend and read: A simple and strong baseline for irregular text recognition. 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 8610–8617. https://doi.org/10.1609/aaai.v33i01.33018610

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Edited Collection

  • Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

    Carneiro, G., Manuel, J., Tavares, R. S., Bradley, A. P., Papa, J. P., Nascimento, J. C., Cardoso, J. S., Lu, Z., & Belagiannis, V. (2019, May 4). Editorial. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, Vol. 7, p. 241. https://doi.org/10.1080/21681163.2019.1594056

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Submitted

  • Forecasting Future Action Sequences with Neural Memory Networks

    Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Forecasting Future Action Sequences with Neural Memory Networks. Retrieved from http://arxiv.org/abs/1909.09278

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  • Deep Declarative Networks: A New Hope

    Gould, S., Hartley, R., & Campbell, D. (2019). Deep Declarative Networks: A New Hope. Retrieved from http://arxiv.org/abs/1909.04866

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