2019 Publications

*bold denotes Core Centre Research Output

Ayton, L. N., Barnes, N., Dagnelie, G., Fujikado, T., Goetz, G., Hornig, R., Petoe, M. A., Jones, B. W., Muqit, M. M.K., Rathbun, D. L., Stingl, K., & Weiland, J.D. (2019, December 10). An update on retinal prostheses. Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2019.11.029

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

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

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

Cherian, A., & Gould, S. (2019). Second-order Temporal Pooling for Action Recognition. International Journal of Computer Vision, 127(4), 340–362. https://doi.org/10.1007/s11263-018-1111-5

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

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

Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2019). Visual Permutation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(12), 3100–3114. https://doi.org/10.1109/TPAMI.2018.2873701

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

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

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 (pp. 6518–6524). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icra.2019.8793608

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

Fernando, T., Ghaemmaghami, H., Denman, S., Sridharan, S., Hussain, N., & Fookes, C. (2019). Heart Sound Segmentation using Bidirectional LSTMs with Attention. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/JBHI.2019.2949516

Gao, Z., Wu, Y., Harandi, M., & Jia, Y. (2019). A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/tnnls.2019.2939177

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

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

Hall, W., Schmader, T., Aday, A., & Croft, E. (2019). Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM. Social Psychological and Personality Science, 10(4), 542–552. https://doi.org/10.1177/1948550618772582

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

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

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

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

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

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

Jin, W., Kulic, D., Lin, J. F.-S., Mou, S., & Hirche, S. (2019). Inverse Optimal Control for Multiphase Cost Functions. IEEE Transactions on Robotics, 1–1. https://doi.org/10.1109/tro.2019.2926388

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

Khaliq, A., Ehsan, S., Chen, Z., Milford, M., & McDonald-Maier, K. (2019). A Holistic Visual Place Recognition Approach Using Lightweight CNNs for Significant ViewPoint and Appearance Changes. IEEE Transactions on Robotics, 1–9. https://doi.org/10.1109/TRO.2019.2956352

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

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

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

Lane, J., Robbins, R. A., Rohan, E. M. F., Crookes, K., Essex, R. W., Maddess, T., Sabeti, F., Mazlin, J.-L., Irons, J., Gradden, T., Dawel, A., Barnes, N., He, X., Smithson, M., McKone, E. (2019). Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration. Journal of Vision, 19(6), 18. https://doi.org/10.1167/19.6.18

Le, H. M., Chin, T.-J., Eriksson, A., Do, T.-T., & Suter, D. (2019). Deterministic Approximate Methods for Maximum Consensus Robust Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2939307 *Early Access

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

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

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

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

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

Liu, C., Yao, R., Rezatofighi, S. H., Reid, I., & Shi, Q. (2019). Model-free tracker for multiple objects using joint appearance and motion inference. IEEE Transactions on Image Processing, 1–1. https://doi.org/10.1109/TIP.2019.2928123

Liu, L., Lu, H., Xiong, H., Xian, K., Cao, Z., & Shen, C. (2019). Counting Objects by Blockwise Classification. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2019.2942970

Liu, W., Gong, D., Tan, M., Shi, Q., Yang, Y., & Hauptmann, A. G. (2019). Learning Distilled Graph for Large-scale Social Network Data Clustering. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2019.2904068

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

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

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

Milford, M., Anthony, S., & Scheirer, W. (2020). Self-Driving Vehicles: Key Technical Challenges and Progress Off the Road. IEEE Potentials, 39(1), 37–45. https://doi.org/10.1109/MPOT.2019.2939376

Molloy, T. L., Inga, J., Flad, M., Ford, J., Perez, T., & Hohmann, S. (2019). Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control. IEEE Transactions on Automatic Control, 1–1. https://doi.org/10.1109/tac.2019.2921835

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

Nascimento, J. C., & Carneiro, G. (2019). One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2922959

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

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

 

 

 

 

 

Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. http://doi.org/10.1109/LRA.2018.2866205

Orlando, J. I., Fu, H., Barbossa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P-A., Kim, J., Lee, J., Lee, J., Li, X., Liu, P., Lu, S., Murugesan, B., Naranjo, V., Phaye, S R., Shankaranarayana, S., Sikka, A., Son,J., van den Hengel, A., Wang, S., Wu, J., Wu, Z., Xu, G., Xu, Y., Yin, P., Li, F., Zhang, X., Yanwu, X., Bogunović, H. (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, 59, 101570. https://doi.org/10.1016/j.media.2019.101570

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

Pan, L., Dai, Y., Liu, M., Porikli, F., & Pan, Q. (2020). Joint stereo video deblurring, scene flow estimation and moving object segmentation. IEEE Transactions on Image Processing, 29, 1748–1761. https://doi.org/10.1109/TIP.2019.2945867

Peng, X., Zhu, H., Feng, J., Shen, C., Zhang, H., & Zhou, J. T. (2019). Deep Clustering With Sample-Assignment Invariance Prior. IEEE Transactions on Neural Networks and Learning Systems, 1–12. https://doi.org/10.1109/tnnls.2019.2958324

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

Radmard, S., Moon, Aj., & Croft, E. A. (2019). Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations. International Journal of Social Robotics, 11(1), 105–121. https://doi.org/10.1007/s12369-018-0480-9

Rahman, M. M., Fookes, C., Baktashmotlagh, M., & Sridharan, S. (2019). Correlation-aware Adversarial Domain Adaptation and Generalization. Pattern Recognition, 107124. https://doi.org/10.1016/j.patcog.2019.107124

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

Rowntree, T., Pontecorvo, C., & Reid, I. (2019). Real-Time Human Gaze Estimation. 2019 Digital Image Computing: Techniques and Applications (DICTA), 1–7. https://doi.org/10.1109/DICTA47822.2019.8945919

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

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

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

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

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. https://doi.org/10.1109/LRA.2018.2884765

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

Wang, X., Shen, C., Li, H., & Xu, S. (2019). Human Detection Aided by Deeply Learned Semantic Masks. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2019.2924912 *Early Access

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

Wei, X.-S., Ye, H.-J., Mu, X., Wu, J., Shen, C., & Zhou, Z.-H. (2019). Multiple Instance Learning with Emerging Novel Class. IEEE Transactions on Knowledge and Data Engineering, 1–1. https://doi.org/10.1109/TKDE.2019.2952588

Wilde, N., Blidaru, A., Smith, S. L., & Kulić, D. (2020). Improving user specifications for robot behavior through active preference learning: Framework and evaluation. The International Journal of Robotics Research, 39(6), 651–667. https://doi.org/10.1177/0278364920910802

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

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

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2020). A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. IEEE Transactions on Medical Imaging, 1–1. https://doi.org/10.1109/tmi.2020.2972964

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

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

Yan, Y., Huang, Y., Chen, S., Shen, C., & Wang, H. (2019). Joint Deep Learning of Facial Expression Synthesis and Recognition. IEEE Transactions on Multimedia, 1–1. https://doi.org/10.1109/TMM.2019.2962317

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

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

Yu, X., Porikli, F., Fernando, B., & Hartley, R. (2019). Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks. International Journal of Computer Vision, 128(2), 500–526. https://doi.org/10.1007/s11263-019-01254-5

Yu, X., Shiri, F., Ghanem, B., & Porikli, F. (2019). Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2019.2914039

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

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. https://doi.org/10.1109/lra.2019.2932579

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

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

Zhang, J., Wu, Q., Zhang, J., Shen, C., Lu, J., & Wu, Q. (2019). Heritage image annotation via collective knowledge. Pattern Recognition, 93, 204–214. https://doi.org/10.1016/j.patcog.2019.04.017

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

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

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

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

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

Zhang, P., Liu, W., Lei, Y., Wang, H., & Lu, H. (2020). Deep Multiphase Level Set for Scene Parsing. IEEE Transactions on Image Processing, 29, 4556–4567. https://doi.org/10.1109/TIP.2019.2957915

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

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

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

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

Zhou, D., Dai, Y., & Li, H. (2020). Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry. IEEE Transactions on Intelligent Transportation Systems, 21(2), 791–802. https://doi.org/10.1109/TITS.2019.2900330

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

Zhou, Y., Li, H., & Kneip, L. (2019). Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment. IEEE Transactions on Robotics, 35(1), 184–199. https://doi.org/10.1109/TRO.2018.2875382

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

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

*bold denotes Core Centre Research Output

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

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

Abedin, A., Rezatofighi, S. H., 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. http://arxiv.org/abs/1906.02399

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

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

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

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

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.

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

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

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

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

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

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

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

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

De Marco, S., Hua, M. D., Mahony, R., & Hamel, T. (2019). Homography estimation of a moving planar scene from direct point correspondence. Proceedings of the IEEE Conference on Decision and Control, 2018-December, 565–570. https://doi.org/10.1109/CDC.2018.8619386

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

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

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

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

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

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

Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019, December 1). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. 2019 Digital Image Computing: Techniques and Applications, DICTA 2019. https://doi.org/10.1109/DICTA47822.2019.8945949

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

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

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

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

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

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

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

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

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

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

Hosseinzadeh, M., Li, K., Latif, Y., & Reid, I. (2019). Real-time monocular object-model aware sparse SLAM. Proceedings – IEEE International Conference on Robotics and Automation, 2019-May, 7123–7129. https://doi.org/10.1109/ICRA.2019.8793728

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

Johnston, A., & Carneiro, G. (2019, December 1). 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

Kennedy, G., Ila, V., & Mahony, R. (2019). 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

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

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

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

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

Li, H., Wang, P., Shen, C., & Zhang, G. (2019). Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8610–8617. https://doi.org/10.1609/aaai.v33i01.33018610

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

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

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

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

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

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

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

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

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

 

 

 

 

 

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

Nash, W., Drummond, T., & Birbilis, N. (2019, May 15). Deep Learning AI for Corrosion Detection. In Proc. CORROSION 2019, 24-28 March, Nashville, Tennessee, USA

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

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

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

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

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

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

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.

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

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

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

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.

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

Roy, S., Harandi, M., Nock, R., & Hartley, R. (2019). Siamese networks: The tale of two manifolds. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 3046–3055. https://doi.org/10.1109/ICCV.2019.00314

Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., & Scaramuzza, D. (2019). CED: Color event camera dataset. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2019-June, 1684–1693. https://doi.org/10.1109/CVPRW.2019.00215

Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Recovering faces from portraits with auxiliary facial attributes. Proceedings – 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, 406–415. https://doi.org/10.1109/WACV.2019.00049

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

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

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.

Stoffregen, T., & Kleeman, L. (2019). Event cameras, contrast maximization and reward functions: An analysis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 12292–12300. https://doi.org/10.1109/CVPR.2019.01258

Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., & Scaramuzza, D. (2019). Event-based motion segmentation by motion compensation. Proceedings of the IEEE International Conference on Computer Vision, 2019-Octob, 7243–7252. https://doi.org/10.1109/ICCV.2019.00734

Tchapmi, L. P., Kosaraju, V., Rezatofighi, H., Reid, I., & Savarese, S. (2019). Topnet: Structural point cloud decoder. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 383–392. https://doi.org/10.1109/CVPR.2019.00047

Teney, D., & Van Den Hengel, A. (2019). Actively seeking and learning from live data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 1940–1949. https://doi.org/10.1109/CVPR.2019.00204

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

Tian, Y., Yu, X., Fan, B., Wu, F., Heijnen, H., & Balntas, V. (2019). Sosnet: Second order similarity regularization for local descriptor learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 11008–11017. https://doi.org/10.1109/CVPR.2019.01127

Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders matter for semantic segmentation: Data-dependent decoding enables flexible feature aggregation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 3121–3130. https://doi.org/10.1109/CVPR.2019.00324

Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9626–9635. https://doi.org/10.1109/ICCV.2019.00972

Tonioni, A., Rahnama, O., Joy, T., DI Stefano, L., Ajanthan, T., & Torr, P. H. S. (2019). Learning to adapt for stereo. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 9653–9662. https://doi.org/10.1109/CVPR.2019.00989

Trumpf, J., Mahony, R., & Hamel, T. (2019). On the structure of kinematic systems with complete symmetry. Proceedings of the IEEE Conference on Decision and Control, 2018-December, 1276–1280. https://doi.org/10.1109/CDC.2018.8619718

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

Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., Yu, G., & Shen, C. (2019). Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8439–8448. https://doi.org/10.1109/ICCV.2019.00853

Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively segmenting instances and semantics in point clouds. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4091–4100. https://doi.org/10.1109/CVPR.2019.00422

Wang, Z., Liu, T., Shi, Q., Kumar, M. P., & Zhang, J. (2019). New convex relaxations for MRF inference with unknown graphs. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 9934–9942. https://doi.org/10.1109/ICCV.2019.01003

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

Xiong, H., Lu, H., Liu, C., Liu, L., Shen, C., & Cao, Z. (2020). From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting. http://arxiv.org/abs/2001.01886

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

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

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

Yin, W., Liu, Y., Shen, C., & Yan, Y. (2019). Enforcing geometric constraints of virtual normal for depth prediction. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 5683–5692. https://doi.org/10.1109/ICCV.2019.00578

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. Proceedings – 2019 International Conference on Computer Vision Workshop, ICCVW 2019, 2893–2902. https://doi.org/10.1109/ICCVW.2019.00351

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

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

Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANET: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 5212–5221. https://doi.org/10.1109/CVPR.2019.00536

Zhang, H., Dai, Y., Li, H., & Koniusz, P. (2019). Deep stacked hierarchical multi-patch network for image deblurring. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 5971–5979. https://doi.org/10.1109/CVPR.2019.00613

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

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

Zhang, K., Luo, W., Ma, L., & Li, H. (2019). Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9203–9210. https://doi.org/10.1609/aaai.v33i01.33019203

Zhang, K., Luo, W., Ma, L., Liu, W., & Li, H. (2019). Learning joint gait representation via quintuplet loss minimization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 4695–4704. https://doi.org/10.1109/CVPR.2019.00483

Zhang, P., Liu, W., Lei, Y., Lu, H., & Yang, X. (2019). Cascaded context pyramid for full-resolution 3D semantic scene completion. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 7800–7809. https://doi.org/10.1109/ICCV.2019.00789

Zhang, X., Cao, J., Shen, C., & You, M. (2019). Self-training with progressive augmentation for unsupervised cross-domain person re-identification. Proceedings of the IEEE International Conference on Computer Vision, 2019-October, 8221–8230. https://doi.org/10.1109/ICCV.2019.00831

Zheng, Y., Yu, X., Liu, M., & Zhang, S. (2019). Residual Multiscale Based Single Image Deraining. Retrieved from https://bmvc2019.org/wp-content/uploads/papers/0168-paper.pdf

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

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

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

*bold denotes Core Centre Research Output

Ajanthan, T., Gupta, K., Torr, P. H. S., Hartley, R., & Dokania, P. K. (2019). Mirror Descent View for Neural Network Quantization. Retrieved from http://arxiv.org/abs/1910.08237

Akhter, I., Fah, C. L., & Hartley, R. (2019). Super-Trajectories: A Compact Yet Rich Video Representation. Retrieved from http://arxiv.org/abs/1901.07273

Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Petersson, L., Gould, S., & Habibian, A. (2019). Learning Variations in Human Motion via Mix-and-Match Perturbation. Retrieved from http://arxiv.org/abs/1908.00733

Aliakbarian, S., Saleh, F. S., Salzmann, M., Petersson, L., & Gould, S. (2019). Sampling Good Latent Variables via CPP-VAEs: VAEs with Condition Posterior as Prior. Retrieved from http://arxiv.org/abs/1912.08521

Anwar, S., & Barnes, N. (2019). Densely Residual Laplacian Super-Resolution. Retrieved from http://arxiv.org/abs/1906.12021

Anwar, S., Khan, S., & Barnes, N. (2019). A Deep Journey into Super-resolution: A survey. Retrieved from http://arxiv.org/abs/1904.07523

Bagchi, S., & Chin, T.-J. (2019). Event-based Star Tracking via Multiresolution Progressive Hough Transforms. Retrieved from http://arxiv.org/abs/1906.07866

Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., & Reid, I. (2019). An Evaluation of Feature Matchers for Fundamental Matrix Estimation. Retrieved from http://arxiv.org/abs/1908.09474

Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, C. S. (2019). Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. Retrieved from https://arxiv.org/pdf/1908.04680

Bustos, A. P., Chin, T.-J., Neumann, F., Friedrich, T., & Katzmann, M. (2019). A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints. Retrieved from http://arxiv.org/abs/1902.01534

Ch’ng, S.-F., Sogi, N., Purkait, P., Chin, T.-J., & Fukui, K. (2019). Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints. Retrieved from http://arxiv.org/abs/1909.11888

Chancán, M., & Milford, M. (2019). From Visual Place Recognition to Navigation: Learning Sample-Efficient Control Policies across Diverse Real World Environments. Retrieved from http://arxiv.org/abs/1910.04335

Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2019). A Compact Neural Architecture for Visual Place Recognition. Retrieved from http://arxiv.org/abs/1910.06840

Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., & Ranzato, M. (2019). Continual Learning with Tiny Episodic Memories. Retrieved from http://arxiv.org/abs/1902.10486

Chen, B., Chin, T.-J., & Li, N. (2019). BPnP: Further Empowering End-to-End Learning with Back-Propagatable Geometric Optimization. Retrieved from http://arxiv.org/abs/1909.06043

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

Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2019). Transductive Zero-Shot Learning for 3D Point Cloud Classification. Retrieved from http://arxiv.org/abs/1912.07161

Cosgun, A., Ditria, L., D’Lima, S., & Drummond, T. (2019). Embracing Contact: Pushing Multiple Objects with Robot’s Forearm. Retrieved from http://arxiv.org/abs/1906.06866

Cosgun, A., Rowntree, T., Reid, I., & Drummond, T. (2019). Practical Robot Learning from Demonstrations using Deep End-to-End Training. Retrieved from https://arxiv.org/pdf/1905.09025

Dasagi, V., Bruce, J., Peynot, T., & Leitner, J. (2019). Ctrl-Z: Recovering from Instability in Reinforcement Learning. Retrieved from http://arxiv.org/abs/1910.03732

Dasagi, V., Lee, R., Bruce, J., & Leitner, J. (2019). Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks. Retrieved from http://arxiv.org/abs/1911.08666

Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixe, L. (2019). CVPR19 Tracking and Detection Challenge: How crowded can it get? Retrieved from http://arxiv.org/abs/1906.04567

Faisal, M., Akhter, I., Ali, M., & Hartley, R. (2019). Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency. Retrieved from http://arxiv.org/abs/1909.13258

Farazi, M. R., Khan, S. H., & Barnes, N. (2019). Question-Agnostic Attention for Visual Question Answering. Retrieved from http://arxiv.org/abs/1908.03289

Felix, R., Sasdelli, M., Reid, I., & Carneiro, G. (2019). Multi-modal Ensemble Classification for Generalized Zero Shot Learning. Retrieved from https://arxiv.org/pdf/1901.04623

Fernando, T., Fookes, C., Denman, S., & Sridharan, S. (2019). Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks. Retrieved from http://arxiv.org/abs/1911.07844

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

Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2019). Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT. Retrieved from http://arxiv.org/abs/1910.10371

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

Hausler, S., Jacobson, A., & Milford, M. (2019). Filter Early, Match Late: Improving Network-Based Visual Place Recognition. Retrieved from https://arxiv.org/abs/1906.12176

Kang, L., Liu, J., Liu, L., Shi, Q., & Ye, D. (2019). Creating Auxiliary Representations from Charge Definitions for Criminal Charge Prediction. Retrieved from http://arxiv.org/abs/1911.05202

Khaliq, A., Ehsan, S., Milford, M., & McDonald-Maier, K. (2019). CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition. Retrieved from http://arxiv.org/abs/1909.08153

Korthals, T., Schilling, M., & Leitner, J. (2019). A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing. Retrieved from http://arxiv.org/abs/1911.00584

Kumar, S., Ghorakavi, R. S., Dai, Y., & Li, H. (2019). Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation. Retrieved from http://arxiv.org/abs/1902.03791

Lassance, C., Latif, Y., Garg, R., Gripon, V., & Reid, I. (2019). Improved Visual Localization via Graph Smoothing. Retrieved from http://arxiv.org/abs/1911.02961

Le, H., Hoang, T., Zhang, Q., Do, T.-T., Eriksson, A., & Milford, M. (2019). SASSE: Scalable and Adaptable 6-DOF Pose Estimation. Retrieved from https://arxiv.org/abs/1902.01549

Lee, N., Ajanthan, T., Gould, S., & Torr, P. H. S. (2019). A Signal Propagation Perspective for Pruning Neural Networks at Initialization. Retrieved from http://arxiv.org/abs/1906.06307

Li, D., Opazo, C. R., Yu, X., & Li, H. (2019). Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison. Retrieved from http://arxiv.org/abs/1910.11006

Li, H., Wang, P., & Shen, C. (2019). Towards End-to-End Text Spotting in Natural Scenes. Retrieved from https://arxiv.org/pdf/1906.06013

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (2019). A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing. Retrieved from https://arxiv.org/pdf/1907.09642

Liu, Y., Dai, Y., Doan, A.-D., Liu, L., & Reid, I. (2019). In defense of OSVOS. Retrieved from http://arxiv.org/abs/1908.06692

Liu, Y., He, T., Chen, H., Wang, X., Luo, C., Zhang, S., Shen, C., & Jin, L. (2019). Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection. http://arxiv.org/abs/1912.09629

Liu, Y., Liu, L., Rezatofighi, H., Do, T.-T., Shi, Q., & Reid, I. (2019). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes. Retrieved from https://arxiv.org/pdf/1901.03796

Liu, Y., Liu, L., Zhang, H., Rezatofighi, H., & Reid, I. (2019). Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation. Retrieved from http://arxiv.org/abs/1909.13046

Liu, Y., Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2019). Photoshopping Colonoscopy Video Frames. Retrieved from http://arxiv.org/abs/1910.10345

Liu, Y., Zhuang, B., Shen, C., Chen, H., & Yin, W. (2019). Training Compact Neural Networks via Auxiliary Overparameterization. Retrieved from http://arxiv.org/abs/1909.02214

Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Index Network. Retrieved from http://arxiv.org/abs/1908.09895

Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2019). Unsupervised Task Design to Meta-Train Medical Image Classifiers. Retrieved from https://arxiv.org/pdf/1907.07816

 

Meng, L., Lin, D., Francey, A., Gorbet, R., Beesley, P., & Kulić, D. (2019). Learning to Engage with Interactive Systems: A field Study. http://arxiv.org/abs/1904.06764

Nazib, A., Fookes, C., & Perrin, D. (2019). Dense Deformation Network for High Resolution Tissue Cleared Image Registration. Retrieved from https://arxiv.org/pdf/1906.06180

Newbury, R., Cosgun, A., Koseoglu, M., & Drummond, T. (2019). Learning to Take Good Pictures of People with a Robot Photographer. Retrieved from https://arxiv.org/pdf/1904.05688

Nguyen, A., Do, T.-T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2019). V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation. Retrieved from http://arxiv.org/abs/1903.10869

Nguyen, C., Do, T.-T., & Carneiro, G. (2019). Uncertainty in Model-Agnostic Meta-Learning using Variational Inference. Retrieved from http://arxiv.org/abs/1907.11864

Opazo, C. R., Marrese-Taylor, E., Saleh, F. S., Li, H., & Gould, S. (2019). Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention. Retrieved from http://arxiv.org/abs/1908.07236

Pan, L., Hartley, R., Scheerlinck, C., Liu, M., Yu, X., & Dai, Y. (2019). Bringing Blurry Alive at High Frame-Rate with an Event Camera. Retrieved from http://arxiv.org/abs/1903.06531

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

Pang, G., Shen, C., Jin, H., & Hengel, A. van den. (2019). Deep Weakly-supervised Anomaly Detection. http://arxiv.org/abs/1910.13601

Purkait, P., Chin, T.-J., & Reid, I. (2019). NeuRoRA: Neural Robust Rotation Averaging. Retrieved from http://arxiv.org/abs/1912.04485

Purkait, P., Zach, C., & Reid, I. (2019). Learning to generate new indoor scenes. Retrieved from http://arxiv.org/abs/1912.04554

Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes. Retrieved from http://arxiv.org/abs/1908.10209

Razjigaev, A., Pandey, A. K., Roberts, J., & Wu, L. (2019). Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm. Retrieved from http://arxiv.org/abs/1903.02217

Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., & Hartley, R. (2019). Learning to Find Common Objects Across Few Image Collections. Retrieved from http://arxiv.org/abs/1904.12936

Shen, T., Gong, D., Zhang, W., Shen, C., & Mei, T. (2019). Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation. Retrieved from http://arxiv.org/abs/1907.12282

Shi, Y., Yu, X., Liu, L., Zhang, T., & Li, H. (2019). Optimal Feature Transport for Cross-View Image Geo-Localization. Retrieved from http://arxiv.org/abs/1907.05021

Shoeiby, M., Aliakbarian, S., Anwar, S., & Petersson, L. (2019). Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network. Retrieved from http://arxiv.org/abs/1909.07577

Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge. Retrieved from http://arxiv.org/abs/1903.07840

Strydom, M., Banach, A., Wu, L., Crawford, R., Roberts, J., & Jaiprakash, A. (2019). Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery. Retrieved from http://arxiv.org/abs/1909.02721

Sünderhauf, N. (2019). Where are the Keys? — Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks. Retrieved from http://arxiv.org/abs/1909.07376

Teney, D., Abbasnejad, E., & Hengel, A. van den. (2019). On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints. Retrieved from http://arxiv.org/abs/1909.13471

Teney, D., Wang, P., Cao, J., Liu, L., Shen, C., & Van Den Hengel, A. (2019). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices. Retrieved from https://arxiv.org/pdf/1907.12271

Tian, Z., Chen, H., & Shen, C. (2019). DirectPose: Direct End-to-End Multi-Person Pose Estimation. Retrieved from http://arxiv.org/abs/1911.07451

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

van Goor, P., Mahony, R., Hamel, T., & Trumpf, J. (2019). An Equivariant Observer Design for Visual Localisation and Mapping. Retrieved from http://arxiv.org/abs/1904.02452

Wang, H., Pang, G., Shen, C., & Ma, C. (2019). Unsupervised Representation Learning by Predicting Random Distances. Retrieved from http://arxiv.org/abs/1912.12186

Wang, W., Xie, E., Sun, P., Wang, W., Tian, L., Shen, C., & Luo, P. (2019). TextSR: Content-Aware Text Super-Resolution Guided by Recognition. Retrieved from http://arxiv.org/abs/1909.07113

Wang, X., Kong, T., Shen, C., Jiang, Y., & Li, L. (2019). SOLO: Segmenting Objects by Locations. Retrieved from http://arxiv.org/abs/1912.04488

Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. van den, Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining. Retrieved from http://arxiv.org/abs/1905.05404

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

Wang, Y., Zhang, H., Liu, Y., Shi, Q., & Zeng, B. (2019). Gradient Information Guided Deraining with A Novel Network and Adversarial Training. Retrieved from http://arxiv.org/abs/1910.03839

Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P. (2019). PolarMask: Single Shot Instance Segmentation with Polar Representation. Retrieved from http://arxiv.org/abs/1909.13226

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2019). Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation. Retrieved from https://arxiv.org/pdf/1903.03313

Xu, Z., Ajanthan, T., & Hartley, R. (2019). Fast and Differentiable Message Passing for Stereo Vision. Retrieved from http://arxiv.org/abs/1910.10892

Yan, C., Pang, G., Bai, X., & Shen, C. (2019). Unified Multifaceted Feature Learning for Person Re-Identification. Retrieved from http://arxiv.org/abs/1911.08651

Yang, L., Wang, P., Li, H., Gao, Y., Zhang, L., Shen, C., & Zhang, Y. (2019). A Simple and Strong Convolutional-Attention Network for Irregular Text Recognition. Retrieved from http://arxiv.org/abs/1904.01375

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., & McDonald-Maier, K. (2019). Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions. Retrieved from https://arxiv.org/abs/1903.09107

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., Alexis, K., & McDonald-Maier, K. (2019). Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics? Retrieved from http://arxiv.org/abs/1904.07967

Zhan, H., Weerasekera, C. S., Bian, J., & Reid, I. (2019). Visual Odometry Revisited: What Should Be Learnt? Retrieved from http://arxiv.org/abs/1909.09803

Zhang, H., Li, Y., Chen, H., & Shen, C. (2019). IR-NAS: Neural Architecture Search for Image Restoration. Retrieved from http://arxiv.org/abs/1909.08228

Zhang, J., Liu, L., Wang, P., & Shen, C. (2019). To Balance or Not to Balance: An Embarrassingly Simple Approach for Learning with Long-Tailed Distributions. Retrieved from http://arxiv.org/abs/1912.04486

Zhang, L., Shi, Z., Zhou, J. T., Cheng, M.-M., Liu, Y., Bian, J.-W., Zeng, Z. & Shen, C. (2019). Ordered or Orderless: A Revisit for Video based Person Re-Identification. Retrieved from http://arxiv.org/abs/1912.11236

Zhang, T., Ji, P., Harandi, M., Huang, W., & Li, H. (2019). Neural Collaborative Subspace Clustering. In ICML 2019 (pp. 7384–7393). Retrieved from http://arxiv.org/abs/1904.10596

Zhang, X., Zhang, R., Cao, J., Gong, D., You, M., & Shen, C. (2019). Part-Guided Attention Learning for Vehicle Re-Identification. Retrieved from http://arxiv.org/abs/1909.06023

Zhao, Y., Liu, Y., Shen, C., Gao, Y., & Xiong, S. (2019). MobileFAN: Transferring Deep Hidden Representation for Face Alignment. Retrieved from http://arxiv.org/abs/1908.03839

Zhu, H., Guo, M., Li, H., Wang, Q., & Robles-Kelly, A. (2019). Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images. Retrieved from http://arxiv.org/abs/1902.05672

Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2019). Training Quantized Neural Networks with the Full-precision Auxiliary Module. Retrieved from http://arxiv.org/abs/1903.11236