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2019 Conference Papers [137]

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

Abbasnejad, E., Wu, Q., Shi, J., & Van Den Hengel, A. (2019). What’s to know? Uncertainty as a Guide to Asking Goal-oriented Questions.

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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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Automatic Classification of Transiting Planet Candidates using Deep Learning

Ansdell, M., Ioannou, Y., Osborn, H. P., Sasdelli, M., Smith, J. C., Caldwell, D., Jenkins, J. M., Räissi, C., & Angerhausen, D. (2019). Automatic Classification of Transiting Planet Candidates using Deep Learning - NASA/ADS. 59. Retrieved from https://ui.adsabs.harvard.edu/abs/2019ASPC..523...59A/abstract

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The NASA FDL Exoplanet Challenge: Transit Classification with Convolutional Neural Networks

Angerhausen, D., Ansdell, M., Osborn, H., Ioannou, Y., Sasdelli, M., Räissi, C., Smith, J. C., Caldwell, D., & Jenkins, J. M. (2019). The NASA FDL Exoplanet Challenge: Transit Classification with Convolutional Neural Networks. 2019 Astrobiology Science Conference. https://agu.confex.com/agu/abscicon19/mediafile/ExtendedAbstract/Paper481561/AbSCiCon_Transits.pdf

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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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Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks

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.

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Morphological networks for image de-raining

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

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Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion

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

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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

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Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion

Ch’ng, S.-F., Khosravian, A., Doan, A.-D., & Chin, T.-J. (2020, January 31). Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion. 7489–7496. https://doi.org/10.1109/iros40897.2019.8967643

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Consensus Maximization Tree Search Revisited

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

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Producing Radiologist-Quality Reports for Interpretable Deep Learning

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

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One-Stage Five-Class Polyp Detection and Classification

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

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End-To-End Diagnosis And Segmentation Learning From Cardiac Magnetic Resonance Imaging

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

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Model Agnostic Saliency For Weakly Supervised Lesion Detection From Breast DCE-MRI

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

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Quantifying the Reality Gap in Robotic Manipulation Tasks

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

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SOSNet: Second Order Similarity Regularization for Local Descriptor Learning

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

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A Dual Joystick-Trackball Interface for Accurate and Time-Efficient Teleoperation of Cable-Driven Parallel Robots within Large Workspaces

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

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Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization

Liu, L., Li, H., & Dai, Y. (2019). Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization.

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Learning Joint Gait Representation via Quintuplet Loss Minimization

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

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Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring

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

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Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

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

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Noise-Aware Unsupervised Deep Lidar-Stereo Fusion

Cheng, X., Zhong, Y., Dai, Y., & Li, H. (2019). Noise-Aware Unsupervised Deep Lidar-Stereo Fusion *.

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Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera

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.

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Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring

Pan, L., Hartley, R., Liu, M., & Dai, Y. (2019). Phase-only Image Based Kernel Estimation for Single Image Blind Deblurring.

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The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation

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.

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A conditional deep generative model of people in natural images

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

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Learning Real-time Closed Loop Robotic Reaching from Monocular Vision by Exploiting A Control Lyapunov Function Structure

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

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A Perception Pipeline for Robotic Harvesting of Green Asparagus

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

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Geometric Feedback Network for Point Cloud Classification

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

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Real-time joint semantic segmentation and depth estimation using asymmetric annotations

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

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Social Robots with Gamification Principles to Increase Long-Term User Interaction

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

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Predictive and adaptive maps for long-term visual navigation in changing environments

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

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Evaluation of Vision-based Surface Crack Detection Methods for Underground Mine Tunnel Images

Azhari, F., Sennersten, C., & Peynot, T. (2019). Evaluation of vision-based surface crack detection methods for underground mine tunnel images. Proceedings of Australasian Conference on Robotics and Automation 2019, Australian Robotics and Automation Association, Adelaide, SA.

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Control Comparison and Evaluation of Pneumatic and Electric Linear Actuators for Configurable Center-Hub Wheels

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.

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Star Tracking using an Event Camera

Chin, T.-J., Bagchi, S., Eriksson, A., & Van Schaik, A. (2019). Star Tracking using an Event Camera.

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Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Salahat, E., Asselineau, C.-A., Coventry, J., & Mahony, R. (2019, December 27). Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms. 763–769. https://doi.org/10.1109/iecon.2019.8927123

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Bushfire emergency response simulation

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

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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.

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Multi-marginal Wasserstein GAN

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

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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

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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.

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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. Retrieved from http://arxiv.org/abs/1904.12936

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Bilinear Attention Networks for Person Retrieval

Fang, P., Zhou, J., Kumar Roy, S., Petersson, L., & Harandi, M. (2019). Bilinear Attention Networks for Person Retrieval.

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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/

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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.

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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.

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Watch, Reason and Code: Learning to Represent Videos Using Program

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

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

Cai, M., Zhan, H., Saroj Weerasekera, C., Li, K., & Reid, I. (2019). Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression. http://openaccess.thecvf.com/content_ICCVW_2019/papers/DL4VSLAM/Cai_Camera_Relocalization_by_Exploiting_Multi-View_Constraints_for_Scene_Coordinates_Regression_ICCVW_2019_paper

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Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene

Li, L., Khan, S., & Barnes ANU, N. (2019). Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene.

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Learning Trajectory Dependencies for Human Motion Prediction

Mao, W., Liu, M., Salzmann, M., & Li, H. (2019). Learning Trajectory Dependencies for Human Motion Prediction.

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CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation

Gupta, K., Petersson, L., & Hartley, R. (2019). CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation. Retrieved from https://github.com/kartikgupta-at-ANU/CullNet.

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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation

Pan, L., Dai, Y., Liu, M., Porikli, F., & Pan, Q. (2019). Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation. Retrieved from http://arxiv.org/abs/1910.02442

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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.

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Unsupervised Primitive Discovery for Improved 3D Generative Modeling

Khan, S. H., Guo, Y., Hayat, M., & Barnes, N. (2019). Unsupervised Primitive Discovery for Improved 3D Generative Modeling. Retrieved from https://salman-h-khan.github.io/papers/CVPR19_2

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Deep Segmentation-Emendation Model for Gland Instance Segmentation

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

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Task-Aware Monocular Depth Estimation for 3D Object Detection

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

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Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation

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

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Parallel Optimal Transport GAN

Avraham, G., Zuo, Y., & Drummond, T. (2019). Parallel Optimal Transport GAN *. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Avraham_Parallel_Optimal_Transport_GAN_CVPR_2019_paper.html

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Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019). Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement.

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From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

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.

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Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

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.

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Exploiting temporal consistency for real-time video depth estimation

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

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Evaluation of the impact of image spatial resolution in designing a context-based fully convolution neural networks for flood mapping

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

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Neighbourhood context embeddings in deep inverse reinforcement learning for predicting pedestrian motion over long time horizons

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

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Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

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.

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Target-Aware Deep Tracking

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

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Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space

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

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Indices Matter: Learning to Index for Deep Image Matting

Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Indices Matter: Learning to Index for Deep Image Matting. Retrieved from https://tinyurl.com/IndexNetV1.

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Enforcing geometric constraints of virtual normal for depth prediction

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

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Scalable Place Recognition Under Appearance Change for Autonomous Driving

Doan, A.-D., Latif, Y., Chin, T.-J., Liu, Y., Do, T.-T., & Reid, I. (2019). Scalable Place Recognition Under Appearance Change for Autonomous Driving.

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Residual Multiscale Based Single Image Deraining

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.

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Proximal Mean-field for Neural Network Quantization

Ajanthan, T., Dokania, P. K., Hartley, R., & Torr, P. H. S. (2019). Proximal Mean-field for Neural Network Quantization. Retrieved from https://github.com/tajanthan/pmf.

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Mind your neighbours: Image annotation with metadata neighbourhood graph co-attention networks

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

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Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning

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.

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Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation

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

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A study of X-vector based speaker recognition on short utterances

Kanagasundaram, A., Sridharan, S., Ganapathy, S., Singh, P., & Fookes, C. B. (2019). A study of X-vector based speaker recognition on short utterances.

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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.

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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

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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

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V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

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

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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

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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

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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

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Model-free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

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

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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

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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 Previous Camera movement Real World Trajectory Embedded Memory Points Embedded Memory Points Network Dense Embedding Correspondences Point-Embeddings CNN Alignment Current Observation Pr.

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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

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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

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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/

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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.

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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.

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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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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.

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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. Retrieved from https://donggong1.github.io/anomdec-memae

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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.

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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

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A Generative Adversarial Density Estimator

Abbasnejad, M. E., Shi, J., Van Den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10782-10791

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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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Min-Max Statistical Alignment for Transfer Learning

Herath, S., Harandi, M., Fernando, B., Nock, R., & Basura, F. (2019). Min-Max Statistical Alignment for Transfer Learning.

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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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Homography estimation of a moving planar scene from direct point correspondence

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

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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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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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On the structure of kinematic systems with complete symmetry

Trumpf, J., Mahony, R., & Hamel, T. (2019). On the structure of kinematic systems with complete symmetry. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2018–Decem, pp. 1276–1280). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CDC.2018.8619718

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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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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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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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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.

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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., & Van Den Hengel, A. (2019). Neighbourhood Watch: Referring Expression Comprehension via Language-guided Graph Attention Networks *.

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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.

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Visual Question Answering as Reading Comprehension

Li, H., Wang, P., Shen, C., & Van Den Hengel, A. (2019). Visual Question Answering as Reading Comprehension.

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Real-Time Monocular Object-Model Aware Sparse SLAM

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

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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.

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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. Retrieved from https://github.com/

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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. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8610–8617. https://doi.org/10.1609/aaai.v33i01.33018610

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