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Scientific Publications

2020All Categories [217]

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks

Rahimi, A., Shaban, A., Cheng, C.-A., Hartley, R., & Boots, B. (2020). Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks. http://arxiv.org/abs/2003.06820

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Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization

Rahimi, A., Shaban, A., Ajanthan, T., Hartley, R., & Boots, B. (2020). Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization. http://arxiv.org/abs/2003.08375

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Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem

Liu, L., Campbell, D., Li, H., Zhou, D., Song, X., & Yang, R. (2020). Learning 2D-3D Correspondences To Solve The Blind Perspective-n-Point Problem. ArXiv. http://arxiv.org/abs/2003.06752

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Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization

Jiang, S., Campbell, D., Liu, M., Gould, S., & Hartley, R. (2020). Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization. ArXiv. http://arxiv.org/abs/2002.11826

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Sub-Instruction Aware Vision-and-Language Navigation

Hong, Y., Rodriguez-Opazo, C., Wu, Q., & Gould, S. (2020). Sub-Instruction Aware Vision-and-Language Navigation. ArXiv. http://arxiv.org/abs/2004.02707

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ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

Saleh, F., Aliakbarian, S., Salzmann, M., & Gould, S. (2020). ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking. http://arxiv.org/abs/2004.07482

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Dynamic SLAM: The Need For Speed

Henein, M., Zhang, J., Mahony, R., & Ila, V. (2020). Dynamic SLAM: The Need For Speed. Proceedings - IEEE International Conference on Robotics and Automation, 2123–2129. http://arxiv.org/abs/2002.08584

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LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision

Zhuang, Z., Yu, X., & Mahony, R. (2020). LyRN (Lyapunov Reaching Network): A Real-Time Closed Loop approach from Monocular Vision. Proceedings - IEEE International Conference on Robotics and Automation, 8331–8337. http://arxiv.org/abs/2005.12072

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UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders

Zhang, J., Fan, D. P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020). UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8579–8588. https://doi.org/10.1109/CVPR42600.2020.00861

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Random Erasing Data Augmentation

Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2020). Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 13001–13008.

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An Observer Design for Visual Simultaneous Localisation and Mapping with Output Equivariance

van Goor, P., Mahony, R., Hamel, T., & Trumpf, J. (2020). An Observer Design for Visual Simultaneous Localisation and Mapping with Output Equivariance. ArXiv. http://arxiv.org/abs/2005.14347

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A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle

Henderson, J., Zamani, M., Mahony, R., & Trumpf, J. (2020). A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle. http://arxiv.org/abs/2009.04630

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Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models

Farazi, M. R., Khan, S. H., & Barnes, N. (2020). Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models. ArXiv. http://arxiv.org/abs/2001.07059

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Bidirectional Self-Normalizing Neural Networks

Lu, Y., Gould, S., & Ajanthan, T. (2020). Bidirectional Self-Normalizing Neural Networks. http://arxiv.org/abs/2006.12169

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Calibration of Neural Networks using Splines

Gupta, K., Rahimi, A., Ajanthan, T., Mensink, T., Sminchisescu, C., & Hartley, R. (2020). Calibration of Neural Networks using Splines. http://arxiv.org/abs/2006.12800

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Correlating edge, pose with parsing

Zhang, Z., Su, C., Zheng, L., & Xie, X. (2020). Correlating edge, pose with parsing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8897–8906. https://doi.org/10.1109/CVPR42600.2020.00892

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Deblurring by Realistic Blurring

Zhang, K., Luo, W., Zhong, Y., Ma, L., Stenger, B., Liu, W., & Li, H. (2020). Deblurring by Realistic Blurring. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2734–2743. https://doi.org/10.1109/CVPR42600.2020.00281

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Mosaic super-resolution via sequential feature pyramid networks

Shoeiby, M., Armin, M. A., Aliakbarian, S., Anwar, S., & Petersson, L. (2020). Mosaic super-resolution via sequential feature pyramid networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 378–387. https://doi.org/10.1109/CVPRW50498.2020.00050

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Transductive zero-shot learning for 3D point cloud classification

Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2020). Transductive zero-shot learning for 3D point cloud classification. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 912–922. https://doi.org/10.1109/WACV45572.2020.9093545

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Reducing the Sim-to-Real Gap for Event Cameras

Stoffregen, T., Scheerlinck, C., Scaramuzza, D., Drummond, T., Barnes, N., Kleeman, L., & Mahony, R. (2020). Reducing the Sim-to-Real Gap for Event Cameras. http://arxiv.org/abs/2003.09078

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Hierarchical Neural Architecture Search for Deep Stereo Matching

Cheng, X., Zhong, Y., Harandi, M., Dai, Y., Chang, X., Drummond, T., Li, H., & Ge, Z. (2020). Hierarchical Neural Architecture Search for Deep Stereo Matching. http://arxiv.org/abs/2010.13501

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Automated corrosion detection using crowdsourced training for deep learning

Nash, W. T., Powell, C. J., Drummond, T., & Birbilis, N. (2020). Automated corrosion detection using crowdsourced training for deep learning. Corrosion, 76(2), 135–141. https://doi.org/10.5006/3397

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Events, Event Prediction, and Predictive Processing

Hohwy, J., Hebblewhite, A., & Drummond, T. (2020). Events, Event Prediction, and Predictive Processing. Topics in Cognitive Science, tops.12491. https://doi.org/10.1111/tops.12491

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Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation

Ju, L., Wang, X., Zhou, Q., Zhu, H., Harandi, M., Bonnington, P., Drummond, T., & Ge, Z. (2020). Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation. ArXiv. http://arxiv.org/abs/2003.10042

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Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning

Loukkal, A., Grandvalet, Y., Drummond, T., & Li, Y. (2020). Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning. http://arxiv.org/abs/2008.04047

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Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

Tong, J., Mahapatra, D., Bonnington, P., Drummond, T., & Ge, Z. (2020). Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12444 LNCS, 41–51. https://doi.org/10.1007/978-3-030-60548-3_5

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Automatic Pruning for Quantized Neural Networks

Guerra, L., Zhuang, B., Reid, I., & Drummond, T. (2020). Automatic Pruning for Quantized Neural Networks. Retrieved from http://arxiv.org/abs/2002.00523

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Learning User Preferences from Corrections on State Lattices

Wilde, N., Kulic, D., & Smith, S. L. (2020). Learning User Preferences from Corrections on State Lattices. Proceedings - IEEE International Conference on Robotics and Automation, 4913–4919. https://doi.org/10.1109/ICRA40945.2020.9197040

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Events and Machine Learning

Hebblewhite, A., Hohwy, J., & Drummond, T. (2020). Events and Machine Learning. Topics in Cognitive Science, tops.12520. https://doi.org/10.1111/tops.12520

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Residual Likelihood Forests

Zuo, Y., & Drummond, T. (2020). Residual Likelihood Forests. http://arxiv.org/abs/2011.02086

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A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Marrese-Taylor, E., Rodriguez-Opazo, C., Balazs, J. A., Gould, S., & Matsuo, Y. (2020). A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews. ACL 2020

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Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching

Shi, Y., Yu, X., Campbell, D., & Li, H. (2020). Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching, CVPR 2020

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DGPose: Deep Generative Models for Human Body Analysis

de Bem, R., Ghosh, A., Ajanthan, T., Miksik, O., Boukhayma, A., Siddharth, N., & Torr, P. (2020). DGPose: Deep Generative Models for Human Body Analysis. International Journal of Computer Vision, 128(5), 1537–1563.

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DPDist : Comparing Point Clouds Using Deep Point Cloud Distance

Urbach, D., Ben-Shabat, Y., & Lindenbaum, M. (2020). DPDist : Comparing Point Clouds Using Deep Point Cloud Distance.

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Inferring Temporal Compositions of Actions Using Probabilistic Automata

Cruz, R. S., Cherian, A., Fernando, B., Campbell, D., & Gould, S. (2020). Inferring Temporal Compositions of Actions Using Probabilistic Automata, CVPR 2020

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Transferring Cross-domain Knowledge for Video Sign Language Recognition

Li, D., Yu, X., Xu, C., Petersson, L., & Li, H. (2020). Transferring Cross-domain Knowledge for Video Sign Language Recognition, CVPR 2020

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EpO-Net: Exploiting geometric constraints on dense trajectories for motion saliency

Faisal, M., Akhter, I., Ali, M., & Hartley, R. (2020). EpO-Net: Exploiting geometric constraints on dense trajectories for motion saliency. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1873–1882).

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Learning to Structure an Image with Few Colors

Hou, Y., Zheng, L., & Gould, S. (2020). Learning to Structure an Image with Few Colors, CVPR 2020

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Joint 3D Instance Segmentation and Object Detection for Autonomous Driving

Zhou, D., Fang, J., Song, X., Liu, L., Yin, J., Dai, Y., Li, H., & Yang, R. (2020). Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1839–1849.

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Equivariant Filter Design for Kinematic Systems on Lie Groups

Mahony, R., & Trumpf, J. (2020). Equivariant Filter Design for Kinematic Systems on Lie Groups. http://arxiv.org/abs/2004.00828

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Equivariant Systems Theory and Observer Design

Mahony, R., Hamel, T., & Trumpf, J. (2020). Equivariant Systems Theory and Observer Design. http://arxiv.org/abs/2006.08276

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Few-shot Action Recognition with Permutation-invariant Attention

Zhang, H., Zhang, L., Qi, X., Li, H., Torr, P. H. S., & Koniusz, P. (2020). Few-shot Action Recognition with Permutation-invariant Attention. 525–542. http://arxiv.org/abs/2001.03905

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Improved Gradient based Adversarial Attacks for Quantized Networks

Gupta, K., & Ajanthan, T. (2020). Improved Gradient based Adversarial Attacks for Quantized Networks. ArXiv. http://arxiv.org/abs/2003.13511

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Post-hoc Calibration of Neural Networks

Rahimi, A., Gupta, K., Ajanthan, T., Mensink, T., Sminchisescu, C., & Hartley, R. (2020). Post-hoc Calibration of Neural Networks. http://arxiv.org/abs/2006.12807

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Localising In Complex Scenes Using Balanced Adversarial Adaptation

Avraham, G., Zuo, Y., & Drummond, T. (2020). Localising In Complex Scenes Using Balanced Adversarial Adaptation. http://arxiv.org/abs/2011.04122

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Joint Estimation of Expertise and Reward Preferences From Human Demonstrations

Carreno-Medrano, P., Smith, S. L., & Kulic, D. (2020). Joint Estimation of Expertise and Reward Preferences From Human Demonstrations. http://arxiv.org/abs/2011.04118

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User Expectations of Robots in Public Spaces: A Co-design Methodology

Tian, L., Carreno-Medrano, P., Sumartojo, S., Mintrom, M., Coronado, E., Venture, G., & Kulić, D. (2020). User Expectations of Robots in Public Spaces: A Co-design Methodology (pp. 259–270). Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_22

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Conditional Convolutions for Instance Segmentation

Tian, Z., Shen, C., & Chen, H. (2020). Conditional Convolutions for Instance Segmentation. http://arxiv.org/abs/2003.05664

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Real-time Image Smoothing via Iterative Least Squares

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (2020). Real-time Image Smoothing via Iterative Least Squares. ACM Transactions on Graphics, 39(3), 1–24. https://doi.org/10.1145/3388887

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OPMP: An Omni-directional Pyramid Mask Proposal Network for Arbitrary-shape Scene Text Detection

Zhang, S., Liu, Y., Jin, L., Wei, Z., & Shen, C. (2020). OPMP: An Omni-directional Pyramid Mask Proposal Network for Arbitrary-shape Scene Text Detection. IEEE Transactions on Multimedia, 1–1. https://doi.org/10.1109/TMM.2020.2978630

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MOT20: A benchmark for multi object tracking in crowded scenes

Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., & Leal-Taixé, L. (2020). MOT20: A benchmark for multi object tracking in crowded scenes. ArXiv. http://arxiv.org/abs/2003.09003

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Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Liu, D., Chen, B., Chin, T.-J., & Rutten, M. (2020). Topological Sweep for Multi-Target Detection of Geostationary Space Objects. http://arxiv.org/abs/2003.09583

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Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection

Pang, G., Yan, C., Shen, C., van den Hengel, A., & Bai, X. (2020). Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12170–12179. https://doi.org/10.1109/CVPR42600.2020.01219

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DeepEMD: Few-shot image classification with differentiable earth mover’s distance and structured classifiers

Zhang, C., Cai, Y., Lin, G., & Shen, C. (2020). DeepEMD: Few-shot image classification with differentiable earth mover’s distance and structured classifiers. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12200–12210. https://doi.org/10.1109/CVPR42600.2020.01222

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MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

Dendorfer, P., Ošep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L. (2020). MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking. http://arxiv.org/abs/2010.07548

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Topological Sweep for Multi-Target Detection of Geostationary Space Objects

Liu, D., Chen, B., Chin, T.-J., & Rutten, M. (2020). Topological Sweep for Multi-Target Detection of Geostationary Space Objects. http://arxiv.org/abs/2003.09583

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SOLOv2: Dynamic, Faster and Stronger

Wang, X., Zhang, R., Kong, T., Li, L., & Shen, C. (2020). SOLOv2: Dynamic, Faster and Stronger. http://arxiv.org/abs/2003.10152

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Multi-way backpropagation for training compact deep neural networks

Guo, Y., Chen, J., Du, Q., Van Den Hengel, A., Shi, Q., & Tan, M. (2020). Multi-way backpropagation for training compact deep neural networks. Neural Networks, 126, 250–261. https://doi.org/10.1016/j.neunet.2020.03.001

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Mask Encoding for Single Shot Instance Segmentation

Zhang, R., Tian, Z., Shen, C., You, M., & Yan, Y. (2020). Mask Encoding for Single Shot Instance Segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 10223–10232. https://doi.org/10.1109/CVPR42600.2020.01024

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Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume

Johnston, A., & Carneiro, G. (2020). Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4755–4764. https://doi.org/10.1109/CVPR42600.2020.00481

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Context Prior for Scene Segmentation

Yu, C., Wang, J., Gao, C., Yu, G., Shen, C., & Sang, N. (2020). Context prior for scene segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12413–12422. https://doi.org/10.1109/CVPR42600.2020.01243

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BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation

Yu, C., Gao, C., Wang, J., Yu, G., Shen, C., & Sang, N. (2020). BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentation. http://arxiv.org/abs/2004.02147

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A Dynamic Parameter Enhanced Network for distant supervised relation extraction

Gou, Y., Lei, Y., Liu, L., Zhang, P., & Peng, X. (2020). A Dynamic Parameter Enhanced Network for distant supervised relation extraction. Knowledge-Based Systems, 197, 105912. https://doi.org/10.1016/j.knosys.2020.105912

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Role-Wise Data Augmentation for Knowledge Distillation

Fu, J., Geng, X., Duan, Z., Zhuang, B., Yuan, X., Trischler, A., Lin, J., Pal, C., & Dong, H. (2020). Role-Wise Data Augmentation for Knowledge Distillation. https://github.com/bigaidream-projects/role-kd

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Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

Teney, D., Abbasnedjad, E., & Hengel, A. van den. (2020). Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision. http://arxiv.org/abs/2004.09034

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Structured Multimodal Attentions for TextVQA

Gao, C., Zhu, Q., Wang, P., Li, H., Liu, Y., Hengel, A. van den, & Wu, Q. (2020). Structured Multimodal Attentions for TextVQA. http://arxiv.org/abs/2006.00753

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Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon

Suter, D., Tennakoon, R., Zhang, E., Chin, T.-J., & Bab-Hadiashar, A. (2020). Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon. http://arxiv.org/abs/2005.05490

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Scene Text Image Super-Resolution in the Wild

Wang, W., Xie, E., Liu, X., Wang, W., Liang, D., Shen, C., & Bai, X. (2020). Scene Text Image Super-Resolution in the Wild. http://arxiv.org/abs/2005.03341

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Scope Head for Accurate L ocalization in Object Detection

Zhan, G., Xu, D., Lu, G., Wu, W., Shen, C., & Ouyang, W. (2020).Scope Head for Accurate L ocalization in Object Detection. http://arxiv.org/abs/2005.04854

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Anomaly Detection via Neighbourhood Contrast. Lecture Notes in Computer Science

Chen, B., Ting, K. M., & Chin, T. J. (2020). Anomaly Detection via Neighbourhood Contrast. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12085 LNAI, 647–659. https://doi.org/10.1007/978-3-030-47436-2_49

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Visual Question Answering with Prior Class Semantics

Shevchenko, V., Teney, D., Dick, A., & Hengel, A. van den. (2020). Visual Question Answering with Prior Class Semantics. Retrieved from http://arxiv.org/abs/2005.01239

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On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law

Teney, D., Kafle, K., Shrestha, R., Abbasnejad, E., Kanan, C., & Hengel, A. van den. (2020). On the Value of Out-of-Distribution Testing: An Example of Goodhart’s Law. Retrieved from http://arxiv.org/abs/2005.09241

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Medical Data Inquiry Using a Question Answering Model

Liao, Z., Liu, L., Wu, Q., Teney, D., Shen, C., van den Hengel, A., & Verjans, J. (2020). Medical Data Inquiry Using a Question Answering Model. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020-April, 1490–1493. https://doi.org/10.1109/ISBI45749.2020.9098531

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Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue

Bian, J.-W., Zhan, H., Wang, N., Chin, T.-J., Shen, C., & Reid, I. (2020). Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue. Retrieved from http://arxiv.org/abs/2006.02708

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Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification

Hermoza, R., Maicas, G., Nascimento, J. C., & Carneiro, G. (2020). Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification. Retrieved from http://arxiv.org/abs/2005.10550

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Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos

Ehsanpour, M., Abedin, A., Saleh, F., Shi, J., Reid, I., & Rezatofighi, H. (2020). Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos. http://arxiv.org/abs/2007.02632

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Deep Learning for Anomaly Detection: A Review

Pang, G., Shen, C., Cao, L., & Hengel, A. van den. (2020). Deep Learning for Anomaly Detection: A Review. http://arxiv.org/abs/2007.02500

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Few-Shot Microscopy Image Cell Segmentation

Dawoud, Y., Hornauer, J., Carneiro, G., & Belagiannis, V. (2020). Few-Shot Microscopy Image Cell Segmentation. Retrieved from http://arxiv.org/abs/2007.01671

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Self-supervised depth estimation to regularise semantic segmentation in knee arthroscopy

Liu, F., Jonmohamadi, Y., Maicas, G., Pandey, A. K., & Carneiro, G. (2020). Self-supervised depth estimation to regularise semantic segmentation in knee arthroscopy. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12261 LNCS, 594–603. https://doi.org/10.1007/978-3-030-59710-8_58

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Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks.

Liu, Y., Liu, L., Wang, P., Zhang, P., & Lei, Y. (2020). Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks. Retrieved from http://arxiv.org/abs/2007.03207

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Scripted Video Generation With a Bottom-Up Generative Adversarial Network

Chen, Q., Wu, Q., Chen, J., Wu, Q., van den Hengel, A., & Tan, M. (2020). Scripted Video Generation With a Bottom-Up Generative Adversarial Network. IEEE Transactions on Image Processing, 29, 7454–7467. https://doi.org/10.1109/TIP.2020.3003227

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Socially and Contextually Aware Human Motion and Pose Forecasting

Adeli, V., Adeli, E., Reid, I., Niebles, J. C., & Rezatofighi, H. (2020). Socially and Contextually Aware Human Motion and Pose Forecasting. IEEE Robotics and Automation Letters, 5(4), 6033–6040. https://doi.org/10.1109/LRA.2020.3010742

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Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors

Abedin, A., Ehsanpour, M., Shi, Q., Rezatofighi, H., & Ranasinghe, D. C. (2020). Attend And Discriminate: Beyond the State-of-the-Art for Human Activity Recognition using Wearable Sensors. Retrieved from http://arxiv.org/abs/2007.07172

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Architecture search of dynamic cells for semantic video segmentation

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2020). Architecture search of dynamic cells for semantic video segmentation. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1959–1968. https://doi.org/10.1109/WACV45572.2020.9093531

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AQD: Towards Accurate Quantized Object Detection

Liu, J., Zhuang, B., Chen, P., Tan, M., & Shen, C. (2020). AQD: Towards Accurate Quantized Object Detection. Retrieved from http://arxiv.org/abs/2007.06919

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Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

Liu, L., Lu, H., Zou, H., Xiong, H., Cao, Z., & Shen, C. (2020). Weighing Counts: Sequential Crowd Counting by Reinforcement Learning. http://arxiv.org/abs/2007.08260

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GADE: A Generative Adversarial Approach to Density Estimation and its Applications.

Abbasnejad, M. E., Shi, J., van den Hengel, A., & Liu, L. (2020). GADE: A Generative Adversarial Approach to Density Estimation and its Applications. International Journal of Computer Vision, 128(10–11), 2731–2743. https://doi.org/10.1007/s11263-020-01360-9

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Reverie: Remote embodied visual referring expression in real indoor environments

Qi, Y., Wu, Q., Anderson, P., Wang, X., Wang, W. Y., Shen, C., & Van Den Hengel, A. (2020). Reverie: Remote embodied visual referring expression in real indoor environments. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 9979–9988. https://doi.org/10.1109/CVPR42600.2020.01000

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Training quantized neural networks with a full-precision auxiliary module

Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2020). Training quantized neural networks with a full-precision auxiliary module. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1485–1494. https://doi.org/10.1109/CVPR42600.2020.00156

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A Robust Attentional Framework for License Plate Recognition in the Wild

Zhang, L., Wang, P., Li, H., Li, Z., Shen, C., & Zhang, Y. (2020). A Robust Attentional Framework for License Plate Recognition in the Wild. IEEE Transactions on Intelligent Transportation Systems, 1–10. https://doi.org/10.1109/tits.2020.3000072

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Quantum Robust Fitting

Chin, T.-J., Suter, D., Chng, S.-F., & Quach, J. (2020). Quantum Robust Fitting. Retrieved from http://arxiv.org/abs/2006.06986

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FCOS: A Simple and Strong Anchor-free Object Detector. IEEE Transactions on Pattern Analysis and Machine Intelligence

Tian, Z., Shen, C., Chen, H., & He, T. (2020). FCOS: A Simple and Strong Anchor-free Object Detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2020.3032166

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Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection

Zhang, J., Xie, Y., Liao, Z., Pang, G., Verjans, J., Li, W., Sun, Z., He, J., Shen, C. & Xia, Y. (2020). Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection. Retrieved from http://arxiv.org/abs/2003.12338

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The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose

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Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training

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Robust Ego and Object 6-DoF Motion Estimation and Tracking

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A Signal Propagation Perspective for Pruning Neural Networks at initialization

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Memorable Maps: A Framework for Re-Defining Places in Visual Place Recognition

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A Review of Current Approaches for UAV Autonomous Mission Planning for Mars Biosignatures Detection.

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Learning Landmark Guided Embeddings for Animal Re-identification

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Learning robust, real-time, reactive robotic grasping

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A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

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Performance improvements of a sweet pepper harvesting robot in protected cropping environments

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Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection

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Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience

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Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection

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Autonomous UAV Navigation for Active Perception of Targets in Uncertain and Cluttered Environments

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Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017

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Active Preference Learning using Maximum Regret

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Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents

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Haptics in Teleoperated Medical Interventions: Force Measurement, Haptic Interfaces and their Influence on User’s Performance

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Context from within: Hierarchical context modeling for semantic segmentation

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Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

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Neural Memory Plasticity for Medical Anomaly Detection

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Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses

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Bio-inspired multi-scale fusion

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Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers

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Learning to Place Objects onto Flat Surfaces in Human-Preferred Orientations

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Joint Deep Cross-Domain Transfer Learning for Emotion Recognition

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A Software System for Human-Robot Interaction To Collect Research Data: A HTML/Javascript Service on the Pepper Robot

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How to Train Your Event Camera Neural Network

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DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

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High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks

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Generative Low-bitwidth Data Free Quantization

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Efficient Semantic Video Segmentation with Per-frame Inference

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PAC-Bayesian Meta-learning with Implicit Prior

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Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

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ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

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Globally Optimal Contrast Maximisation for Event-based Motion Estimation

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On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering

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Learning Deep Gradient Descent Optimization for Image Deconvolution

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3D Gated Recurrent Fusion for Semantic Scene Completion

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Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning

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DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

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Learn to Predict Sets Using Feed-Forward Neural Networks

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Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

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Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory

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From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

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Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation

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Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation

Gong, D., Sun, W., Shi, Q., Van Den Hengel, A., & Zhang, Y. (2020). Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation.https://arxiv.org/pdf/2001.02381.pdf

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Discrimination-aware Network Pruning for Deep Model Compression

Liu, J., Zhuang, B., Zhuang, Z., Guo, Y., Huang, J., Zhu, J., & Tan, M. (2020). Discrimination-aware Network Pruning for Deep Model Compression. Retrieved from https://github.com/SCUT-AILab/DCP.

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BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., & Yan, Y. (2020). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation. Retrieved from http://arxiv.org/abs/2001.00309

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Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., Micheloni, C., Carneiro, G., & Fontanarosa, D. (2020). Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Medical Image Analysis, 60. https://doi.org/10.1016/j.media.2019.101631

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Fast Image Reconstruction with an Event Camera

Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R. E., & Scaramuzza, D. (2020). Fast Image Reconstruction with an Event Camera. Retrieved from https://github.com/uzh-rpg/rpg

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Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization

Hua, M. D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2020). Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization. Automatica, 115, 1–10.

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Switchable Precision Neural Networks

Guerra, L., Zhuang, B., Reid, I., & Drummond, T. (2020). Switchable Precision Neural Networks. Retrieved from http://arxiv.org/abs/2002.02815

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Automatic Pruning for Quantized Neural Networks

Guerra, L., Zhuang, B., Reid, I., & Drummond, T. (2020). Automatic Pruning for Quantized Neural Networks. Retrieved from http://arxiv.org/abs/2002.00523

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OpenGAN: Open Set Generative Adversarial Networks

Ditria, L., Meyer, B. J., & Drummond, T. (2020). OpenGAN: Open Set Generative Adversarial Networks. Retrieved from http://arxiv.org/abs/2003.08074

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Low-cost PM2. 5 Sensors: An Assessment of Their Suitability for Various Applications

Jayaratne, R., Liu, X., Ahn, K.-H., Asumadu-Sakyi, A., Fisher, G., Gao, J., Mabon, A., Mazaheri, M., Mullins, B., Nyaku, M., Ristovki, Z., Scorgie, Y., Thai, P., Dunbabin, M., & Morawska, L. (2020). Low-cost PM 2.5 Sensors: An Assessment of their Suitability for Various Applications. Aerosol and Air Quality Research, 20, 520–532. https://doi.org/10.4209/aaqr.2018.10.0390

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String stable integral control of vehicle platoons with disturbances

Silva, G. F., Donaire, A., McFadyen, A., & Ford, J. (2020). String stable integral control of vehicle platoons with disturbances. Retrieved from http://arxiv.org/abs/2002.09666

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Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats

Sekar, S., Panchal, S. K., Ghattamaneni, N. K., Brown, L., Crawford, R., Xiao, Y., & Prasadam, I. (2020). Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats. Nutrients, 12(2), 509. https://doi.org/10.3390/nu12020509

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Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning

Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., Roberts, J., Pandey, A.K., & Carneiro, G. (2020). Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.2980025

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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., … Jung, H. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open, 3(3), e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265

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LSTM guided ensemble correlation filter tracking with appearance model pool

Jain, M., Subramanyam, A. V., Denman, S., Sridharan, S., & Fookes, C. (2020). LSTM guided ensemble correlation filter tracking with appearance model pool. Computer Vision and Image Understanding, 195, 102935. https://doi.org/10.1016/j.cviu.2020.102935

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Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification.

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A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

Nazib, A., Fookes, C., Salvado, O., & Perrin, D. (2020). A Multiple Decoder CNN for Inverse Consistent 3D Image Registration. Retrieved from http://arxiv.org/abs/2002.06468

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Enhancing Feature Invariance with Learned Image Transformations for Image Retrieval

Tursun, O., Denman, S., Sridharan, S., & Fookes, C. (2020). Enhancing Feature Invariance with Learned Image Transformations for Image Retrieval. Retrieved from http://arxiv.org/abs/2002.01642

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EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation

Morrison, D., Corke, P., & Leitner, J. (2020). EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulation. Retrieved from http://arxiv.org/abs/2003.01314

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Maximising Manipulability During Resolved-Rate Motion Control

Haviland, J., & Corke, P. (2020). Maximising Manipulability During Resolved-Rate Motion Control. Retrieved from http://arxiv.org/abs/2002.11901

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Robot Navigation in Unseen Spaces using an Abstract Map

Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. (2020). Robot Navigation in Unseen Spaces using an Abstract Map. Retrieved from http://arxiv.org/abs/2001.11684

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Multiplicative Controller Fusion: A Hybrid Navigation Strategy For Deployment in Unknown Environments

Rana, K., Dasagi, V., Talbot, B., Milford, M., & Sünderhauf, N. (2020). Multiplicative Controller Fusion: A Hybrid Navigation Strategy For Deployment in Unknown Environments. Retrieved from http://arxiv.org/abs/2003.05117

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MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation

Chancán, M., & Milford, M. (2020). MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation. Retrieved from http://arxiv.org/abs/2003.00667

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Hierarchical Multi-Process Fusion for Visual Place Recognition

Hausler, S., & Milford, M. (2020). Hierarchical Multi-Process Fusion for Visual Place Recognition. Retrieved from http://arxiv.org/abs/2002.03895

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A Hybrid Compact Neural Architecture for Visual Place Recognition

Chancan, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2020). A Hybrid compact neural architecture for visual place recognition. IEEE Robotics and Automation Letters, 5(2), 993–1000. https://doi.org/10.1109/LRA.2020.2967324

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Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M. J., & McDonald-Maier, K. D. (2020). Exploring performance bounds of visual place recognition using extended precision. IEEE Robotics and Automation Letters, 5(2), 1688–1695. https://doi.org/10.1109/LRA.2020.2969197

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CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

Zaffar, M., Ehsan, S., Milford, M., & McDonald-Maier, K. (2020). CoHOG: A light-weight, compute-efficient, and training-free visual place recognition technique for changing environments. IEEE Robotics and Automation Letters, 5(2), 1835–1842. https://doi.org/10.1109/LRA.2020.2969917

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Model-free vision-based shaping of deformable plastic materials

Cherubini, A., Ortenzi, V., Cosgun, A., Lee, R., & Corke, P. (2020). Model-free vision-based shaping of deformable plastic materials. The International Journal of Robotics Research, 027836492090768. https://doi.org/10.1177/0278364920907684

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Hierarchical Attention Network for Action Segmentation

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2020). Hierarchical Attention Network for Action Segmentation. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2020.01.023

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Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park, C., Moghadam, P., Kim, S., Sridharan, S., & Fookes, C. (2020). Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach. IEEE Robotics and Automation Letters, 1–1. https://doi.org/10.1109/LRA.2020.2969164

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Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study

Vickers, M. L., Ballard, E. L., Harris, P. N. A., Knibbs, L. D., Jaiprakash, A., Dulhunty, J. M., … Parkinson, B. (2020). Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study. International Journal of Environmental Research and Public Health, 17(2), 657. https://doi.org/10.3390/ijerph17020657

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Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy

Antico, M., Fontanarosa, D., Carneiro, G., Vukovic, D., Camps, S. M., Sasazawa, F., … Crawford, R. (2020). Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. https://doi.org/10.1109/TUFFC.2020.2965291

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Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations

Garg, S., & Milford, M. (2020). Fast, Compact and Highly Scalable Visual Place Recognition through Sequence-based Matching of Overloaded Representations. Retrieved from http://arxiv.org/abs/2001.08434

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Towards Surgical Robots: Understanding Interaction Challenges in Knee Surgery

Opie, J., Jaiprakash, A., Ploderer, B., Brereton, M., & Roberts, J. (2019). Towards Surgical Robots. Proceedings of the 31st Australian Conference on Human-Computer-Interaction, 255–265. https://doi.org/10.1145/3369457.3370916

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Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation

Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse Range Estimation. Retrieved from http://arxiv.org/abs/2001.00330

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A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Hinas, A., Ragel, R., Roberts, J., & Gonzalez, F. (2020). A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS. Sensors, 20(1), 272. https://doi.org/10.3390/s20010272

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Architecture Search of Dynamic Cells for Semantic Video Segmentation

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2020). Architecture Search of Dynamic Cells for Semantic Video Segmentation.

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Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Nekrasov, V., Shen, C., & Reid, I. (2020). Template-Based Automatic Search of Compact Semantic Segmentation Architectures.

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