2020 Publications

Lecture Notes

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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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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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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Pairwise Relation Learning for Semi-supervised Gland Segmentation

Xie, Y., Zhang, J., Liao, Z., Verjans, J., Shen, C., & Xia, Y. (2020). Pairwise Relation Learning for Semi-supervised Gland Segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12265 LNCS, 417–427. https://doi.org/10.1007/978-3-030-59722-1_40
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Book Section

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

Suddrey, G., & Robinson, N. (2020). A Software System for Human-Robot Interaction To Collect Research Data. Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 459–461. https://doi.org/10.1145/3371382.3378287
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Scientific Publications

Fracture-related infection: current methods for prevention and treatment

Foster, A. L., Moriarty, T. F., Trampuz, A., Jaiprakash, A., Burch, M. A., Crawford, R., Paterson, D. L., Metsemakers, W-J., Schuetz, M., & Richards, R. G. (2020). Fracture-related infection: current methods for prevention and treatment. Expert Review of Anti-Infective Therapy, 18(4), 307–321. https://doi.org/10.1080/14787210.2020.1729740
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On the informativeness of measurements in Shiryaev’s Bayesian quickest change detectio

Ford, J. J., James, J., & Molloy, T. L. (2020). On the informativeness of measurements in Shiryaev’s Bayesian quickest change detection. Automatica, 111, 108645. https://doi.org/10.1016/j.automatica.2019.108645
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Event-based visual place recognition with ensembles of temporal windows

Fischer, T., & Milford, M. (2020). Event-based visual place recognition with ensembles of temporal windows. IEEE Robotics and Automation Letters, 5(4), 6924–6931. https://doi.org/10.1109/LRA.2020.3025505
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Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface

Fielding, A. L., Pandey, A. K., Jonmohamadi, Y., Via, R., Weber, D. C., Lomax, A. J., & Fattori, G. (2020). Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface. IEEE Sensors Journal, 1–1. https://doi.org/10.1109/jsen.2020.2993264
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Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Fernando, T., Sridharan, S., McLaren, M., Priyasad, D., Denman, S., & Fookes, C. (2020). Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection. IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 1159–1169. https://doi.org/10.1109/TASLP.2020.2982297
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Detection of Fake and Fraudulent Faces via Neural Memory Networks

Fernando, T., Fookes, C., Denman, S., & Sridharan, S. (2020). Detection of Fake and Fraudulent Faces via Neural Memory Networks. IEEE Transactions on Information Forensics and Security, 16, 1973–1988. https://doi.org/10.1109/TIFS.2020.3047768
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Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020). Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion. IEEE Signal Processing Magazine, 38(1), 87–96. https://doi.org/10.1109/MSP.2020.2988287
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Why are Generative Adversarial Networks so Fascinating and Annoying?

Faria, F. A., & Carneiro, G. (2020). Why are Generative Adversarial Networks so Fascinating and Annoying? Proceedings - 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2020, 1–8. https://doi.org/10.1109/SIBGRAPI51738.2020.00009
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From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concept

Farazi, M. R., Khan, S. H., & Barnes, N. (2020). From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concepts. Image and Vision Computing, 103, 103985. https://doi.org/10.1016/j.imavis.2020.103985
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A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Ghaemmaghami, H., & Fookes, C. (2020). A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/jbhi.2020.3027910
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Domain Generalization in Biosignal Classification

Dissanayake, T., Fernando, T., Denman, S., Ghaemmaghami, H., Sridharan, S., & Fookes, C. (2020). Domain Generalization in Biosignal Classification. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2020.3045720
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Learning Object Relation Graph and Tentative Policy for Visual Navigation

Du H., Yu X., Zheng L. (2020) Learning Object Relation Graph and Tentative Policy for Visual Navigation. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_2
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Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator

Dhanani, J., Pang, G., Pincus, J., Ahern, B., Goodwin, W., Cowling, N., Whitten, G., Abdul-Aziz, M. H., Martin, S., Corke, P., & Laupland, K. B. (2020). Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator. BMC Research Notes, 13(1), 1–6. https://doi.org/10.1186/s13104-020-05259-z
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Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path

Deremetz, M., Lenain, R., Laneurit, J., Debain, C., & Peynot, T. (2020). Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path. CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, 372–379. https://doi.org/10.1109/CCTA41146.2020.9206153
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Towards Light‐Weight Portrait Matting via Parameter Sharing

Dai, Y., Lu, H., & Shen, C. (2020). Towards Light‐Weight Portrait Matting via Parameter Sharing. Computer Graphics Forum, cgf.14179. https://doi.org/10.1111/cgf.14179
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Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n

Dellaert F., Rosen D.M., Wu J., Mahony R., Carlone L. (2020) Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_18
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The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry

Coory, J. A., Tan, K. G., Whitehouse, S. L., Hatton, A., Graves, S. E., & Crawford, R. W. (2020). The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(1), 132–138. https://doi.org/10.1016/j.arth.2019.08.007
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Benchmarking Simulated Robotic Manipulation through a Real World Dataset

Collins, J., McVicar, J., Wedlock, D., Brown, R., Howard, D., & Leitner, J. (2020). Benchmarking Simulated Robotic Manipulation through a Real World Dataset. IEEE Robotics and Automation Letters, 5(1), 250–257. https://doi.org/10.1109/LRA.2019.2953663
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A robotic vision system for inspection of soiling at CSP plants

Coventry, J., Asselineau, C. A., Salahat, E., Raman, M. A., & Mahony, R. (2020). A robotic vision system for inspection of soiling at CSP plants. AIP Conference Proceedings, 2303(1), 100001. https://doi.org/10.1063/5.0029493
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Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution

Chan, W. P., Sakr, M., Quintero, C. P., Croft, E., & Van Der Loos, H. F. M. H. (2020). Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution. 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, 419–424. https://doi.org/10.1109/RO-MAN47096.2020.9223526
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Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry

Calabro, L., Yong, M., Whitehouse, S. L., Hatton, A., de Steiger, R., & Crawford, R. W. (2020). Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(9), 2518–2524. https://doi.org/10.1016/j.arth.2020.04.027
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Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization

Campbell D., Liu L., Gould S. (2020) Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_15
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A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps

Cagara, D., Dunbabin, M., & Rigby, P. (2020). A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps. Proceedings - IEEE International Conference on Robotics and Automation, 8573–8579. https://doi.org/10.1109/ICRA40945.2020.9196680
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Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis

Blythe, R., O’Gorman, P. M., Crawford, R. W., Feenan, R., Hatton, A., Whitehouse, S. L., & Graves, N. (2020). Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis. Journal of Arthroplasty, 35(6), 1614–1621. https://doi.org/10.1016/j.arth.2020.02.001
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Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Banach, A., Strydom, M., Jaiprakash, A., Carneiro, G., Brown, C., Crawford, R., & McFadyen, A. (2020). Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms. IEEE Access, 8, 213378–213388. https://doi.org/10.1109/ACCESS.2020.3040187
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Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors

Arthur, J. N., Chaudhry, M. U., Woodruff, M. A., Pandey, A. K., & Yambem, S. D. (2020). Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors. Advanced Electronic Materials, 6(5), 1901079. https://doi.org/10.1002/aelm.201901079
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Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy

Antico, M., Vukovic, D., Camps, S. M., Sasazawa, F., Takeda, Y., Le, A. T. H., Jaiprakash, A., Roberts, J., Crawford, R., Fontanarosa, D., & Carneiro, G. (2020). Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2543–2552. https://doi.org/10.1109/TUFFC.2020.2965291
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4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study

Antico, M., Sasazawa, F., Takeda, Y., Jaiprakash, A. T., Wille, M. L., Pandey, A. K., Crawford, R., & Fontanarosa, D. (2020). 4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study. IEEE Access, 8, 146331–146341. https://doi.org/10.1109/ACCESS.2020.3014999
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Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy

Antico, M., Sasazawa, F., Dunnhofer, M., Camps, S. M., Jaiprakash, A. T., Pandey, A. K., Crawford, R., Carneiro, G., & Fontanarosa, D. (2020). Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy. Ultrasound in Medicine and Biology, 46(2), 422–435. https://doi.org/10.1016/j.ultrasmedbio.2019.10.015
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Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery

Ali, S., Jonmohamadi, Y., Takeda, Y., Roberts, J., Crawford, R., & Pandey, A. K. (2020). Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2020.3037301
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Neural Memory Networks for Seizure Type Classification

Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M. J., & Fookes, C. (2020). Neural Memory Networks for Seizure Type Classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 569–575. https://doi.org/10.1109/EMBC44109.2020.9175641
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Attention Networks for Multi-Task Signal Analysis

Ahmedt-Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2020). Attention Networks for Multi-Task Signal Analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 184–187. https://doi.org/10.1109/EMBC44109.2020.9175730
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MobileFAN: Transferring Deep Hidden Representation for Face Alignment

Zhao, Y., Liu, Y., Shen, C., Gao, Y., & Xiong, S. (2020). MobileFAN: Transferring deep hidden representation for face alignment. Pattern Recognition, 100, 107114. https://doi.org/10.1016/j.patcog.2019.107114
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Deep Multiphase Level Set for Scene Parsing

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

Zhang, H., Li, Y., Chen, H., & Shen, C. (2020). Memory-efficient hierarchical neural architecture search for image denoising. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3654–3663. https://doi.org/10.1109/CVPR42600.2020.00371
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Visual Odometry Revisited: What Should Be Learnt?

Zhan, H., Weerasekera, C. S., Bian, J. W., & Reid, I. (2020). Visual Odometry Revisited: What Should Be Learnt? Proceedings - IEEE International Conference on Robotics and Automation, 4203–4210. https://doi.org/10.1109/ICRA40945.2020.9197374
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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2020). A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. IEEE Transactions on Medical Imaging, 39(7), 2482–2493. https://doi.org/10.1109/TMI.2020.2972964
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PolarMask: Single Shot Instance Segmentation with Polar Representation

Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., & Luo, P. (2020). PolarMask: Single shot instance segmentation with polar representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12190–12199. https://doi.org/10.1109/CVPR42600.2020.01221
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Improving user specifications for robot behavior through active preference learning: Framework and evaluation

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

Wang X., Kong T., Shen C., Jiang Y., Li L. (2020) SOLO: Segmenting Objects by Locations. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_38
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Optimal Feature Transport for Cross-View Image Geo-Localization

Shi, Y., Yu, X., Liu, L., Zhang, T., & Li, H. (2020). Optimal Feature Transport for Cross-View Image Geo-Localization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11990-11997. https://doi.org/10.1609/aaai.v34i07.6875
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Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

Nguyen, C., Do, T. T., & Carneiro, G. (2020). Uncertainty in model-agnostic meta-learning using variational inference. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 3079–3089. https://doi.org/10.1109/WACV45572.2020.9093536
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Unsupervised Task Design to Meta-Train Medical Image Classifiers

Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2020). Unsupervised Task Design to Meta-Train Medical Image Classifiers. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1339–1342. https://doi.org/10.1109/ISBI45749.2020.9098470
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Index Network

Lu, H., Dai, Y., Shen, C., & Xu, S. (2019, August 11). Index network. ArXiv. https://doi.org/10.1109/tpami.2020.3004474
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Photoshopping Colonoscopy Video Frames

Liu, Y., Tian, Y., Maicas, G., Cheng Tao Pu, L. Z., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Photoshopping Colonoscopy Video Frames. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1642–1646. https://doi.org/10.1109/ISBI45749.2020.9098406
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A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (2019, July 22). A generalized framework for edge-preserving and structure-preserving image smoothing. ArXiv, Vol. 34, pp. 11620–11628. https://doi.org/10.1609/aaai.v34i07.6830
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Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Li, D., Opazo, C. R., Yu, X., & Li, H. (2020). Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1448–1458. https://doi.org/10.1109/WACV45572.2020.9093512
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Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2020). Semi-Supervised Multi-Domain Multi-Task Training for Metastatic Colon Lymph Node Diagnosis from Abdominal CT. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1478–1481. https://doi.org/10.1109/ISBI45749.2020.9098372
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End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

Chen, B., Parra, Á., Cao, J., Li, N., & Chin, T. J. (2020). End-to-end learnable geometric vision by backpropagating PNP optimization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8097–8106. https://doi.org/10.1109/CVPR42600.2020.00812
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CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

Chancan, M., & Milford, M. (2020). CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning. Proceedings - IEEE International Conference on Robotics and Automation, 1697–1704. https://doi.org/10.1109/ICRA40945.2020.9197336
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Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraint

Ch’ng, S. F., Sogi, N., Purkait, P., Chin, T. J., & Fukui, K. (2020). Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints∗. Proceedings - IEEE International Conference on Robotics and Automation, 9680–9686. https://doi.org/10.1109/ICRA40945.2020.9196902
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A Deep Journey into Super-resolution: A survey

Anwar, S., Khan, S., & Barnes, N. (2020, June 1). A Deep Journey into Super-resolution: A Survey. ACM Computing Surveys, Vol. 53, pp. 1–34. https://doi.org/10.1145/3390462
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Densely Residual Laplacian Super-Resolution

Anwar, S., & Barnes, N. (2019, June 27). Densely Residual Laplacian Super-Resolution. ArXiv. https://doi.org/10.1109/tpami.2020.3021088
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Component-based Attention for Large-scale Trademark Retrieval

Tursun, O., Denman, S., Sivapalan, S., Sridharan, S., Fookes, C., & Mau, S. (2019). Component-based Attention for Large-scale Trademark Retrieval. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2019.2959921
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Towards Effective Deep Embedding for Zero-Shot Learning

Zhang, L., Wang, P., Liu, L., Shen, C., Wei, W., Zhang, Y., & Van Den Hengel, A. (2020). Towards Effective Deep Embedding for Zero-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology, 30(9), 2843–2852. https://doi.org/10.1109/TCSVT.2020.2984666
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Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

Hoang, T., Do, T. T., Le, H., Le-Tan, D. K., & Cheung, N. M. (2020). Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing. Computer Vision and Image Understanding, 191, 102852. https://doi.org/10.1016/j.cviu.2019.102852
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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

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Calibration of Neural Networks using Splines

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Correlating edge, pose with parsing

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Deblurring by Realistic Blurring

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Mosaic super-resolution via sequential feature pyramid networks

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Transductive zero-shot learning for 3D point cloud classification

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Reducing the Sim-to-Real Gap for Event Cameras

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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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Events, Event Prediction, and Predictive Processing

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

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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

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Automatic Pruning for Quantized Neural Networks

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Learning User Preferences from Corrections on State Lattices

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Events and Machine Learning

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Residual Likelihood Forests

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

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

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DGPose: Deep Generative Models for Human Body Analysis

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DPDist : Comparing Point Clouds Using Deep Point Cloud Distance

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Inferring Temporal Compositions of Actions Using Probabilistic Automata

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Transferring Cross-domain Knowledge for Video Sign Language Recognition

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

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Learning to Structure an Image with Few Colors

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Joint 3D Instance Segmentation and Object Detection for Autonomous Driving

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Equivariant Filter Design for Kinematic Systems on Lie Groups

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Equivariant Systems Theory and Observer Design

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Few-shot Action Recognition with Permutation-invariant Attention

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Improved Gradient based Adversarial Attacks for Quantized Networks

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Post-hoc Calibration of Neural Networks

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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

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

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Conditional Convolutions for Instance Segmentation

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Real-time Image Smoothing via Iterative Least Squares

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

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MOT20: A benchmark for multi object tracking in crowded scenes

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Topological Sweep for Multi-Target Detection of Geostationary Space Objects

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

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

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MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

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Topological Sweep for Multi-Target Detection of Geostationary Space Objects

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SOLOv2: Dynamic, Faster and Stronger

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Multi-way backpropagation for training compact deep neural networks

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Mask Encoding for Single Shot Instance Segmentation

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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

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

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A Dynamic Parameter Enhanced Network for distant supervised relation extraction

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Role-Wise Data Augmentation for Knowledge Distillation

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Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

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Structured Multimodal Attentions for TextVQA

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

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Scene Text Image Super-Resolution in the Wild

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Scope Head for Accurate L ocalization in Object Detection

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Anomaly Detection via Neighbourhood Contrast. Lecture Notes in Computer Science

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Visual Question Answering with Prior Class Semantics

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

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Medical Data Inquiry Using a Question Answering Model

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

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

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

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Deep Learning for Anomaly Detection: A Review

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Few-Shot Microscopy Image Cell Segmentation

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

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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

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Socially and Contextually Aware Human Motion and Pose Forecasting

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

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Architecture search of dynamic cells for semantic video segmentation

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AQD: Towards Accurate Quantized Object Detection

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Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

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

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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

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A Robust Attentional Framework for License Plate Recognition in the Wild

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Quantum Robust Fitting

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

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

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Soft Expert Reward Learning for Vision-and-Language Navigation

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Object-and-Action Aware Model for Visual Language Navigation

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Improving Generative Adversarial Networks with Local Coordinate Coding

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Guided Co-Segmentation Network for Fast Video Object Segmentation

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AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

Wang, W., Liu, X., Ji, X., Xie, E., Liang, D., Yang, Z., Lu, T., Shen, C., & Luo, P. (2020). AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting. Retrieved from http://arxiv.org/abs/2008.00714
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Pairwise Relation Learning for Semi-supervised Gland Segmentation

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How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?

Rezatofighi, H., Nguyen, T. T. D., Vo, B.-N., Vo, B.-T., Savarese, S., & Reid, I. (2020). How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks? Retrieved from http://arxiv.org/abs/2008.03533
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A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots

Mao, J., Hu, X., Zhang, L., He, X., & Milford, M. (2020). A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots. Journal of Intelligent and Robotic Systems: Theory and Applications, 100(1), 289–310. https://doi.org/10.1007/s10846-020-01190-4
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Towards Simulating Semantic Onboard UAV Navigation

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

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Fast postprocessing for difficult discrete energy minimization problems

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Single Image Optical Flow Estimation with an Event Camera

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Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

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Geometry to the Rescue : 3D Instance Reconstruction from a Cluttered Scene

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Guest Editorial: Special Issue on ACCV 2018

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Rethinking Class Relations: Absolute-relative Few-shot Learning

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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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Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

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From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction

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A Signal Propagation Perspective for Pruning Neural Networks at initialization

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Learning Landmark Guided Embeddings for Animal Re-identification

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JFR special issue on agricultural robotics, part 2

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Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection

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

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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

"Gustavo Carneiro, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Alastair Burt,
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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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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Deep Clustering With Sample-Assignment Invariance Prior

Peng, X., Zhu, H., Feng, J., Shen, C., Zhang, H., & Zhou, J. T. (2020). Deep Clustering with Sample-Assignment Invariance Prior. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4857–4868. https://doi.org/10.1109/TNNLS.2019.2958324
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Multiple Instance Learning with Emerging Novel Class

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

Yan, Y., Huang, Y., Chen, S., Shen, C., & Wang, H. (2020). Joint Deep Learning of Facial Expression Synthesis and Recognition. IEEE Transactions on Multimedia, 22(11), 2792–2807. https://doi.org/10.1109/TMM.2019.2962317
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Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces

Yu, X., Shiri, F., Ghanem, B., & Porikli, F. (2020). Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2148–2164. https://doi.org/10.1109/TPAMI.2019.2914039
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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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Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Molloy, T. L., Inga, J., Flad, M., Ford, J. J., Perez, T., & Hohmann, S. (2020). Inverse open-loop noncooperative differential games and inverse optimal control. IEEE Transactions on Automatic Control, 65(2), 897–904. https://doi.org/10.1109/TAC.2019.2921835
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An Update on Retinal Prostheses

Ayton, L. N., Barnes, N., Dagnelie, G., Fujikado, T., Goetz, G., Hornig, R., Jones, B. W., Muqit, M. M. K., Rathbun, D. L., Stingl, K., Weiland, J. D., & Petoe, M. A. (2020). An update on retinal prostheses. In Clinical Neurophysiology (Vol. 131, Issue 6, pp. 1383–1398). Elsevier Ireland Ltd. https://doi.org/10.1016/j.clinph.2019.11.029
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Correlation-aware Adversarial Domain Adaptation and Generalization

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

Fernando, T., Ghaemmaghami, H., Denman, S., Sridharan, S., Hussain, N., & Fookes, C. (2020). Heart Sound Segmentation Using Bidirectional LSTMs with Attention. IEEE Journal of Biomedical and Health Informatics, 24(6), 1601–1609. https://doi.org/10.1109/JBHI.2019.2949516
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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. (2020). Joint stereo video deblurring, scene flow estimation and moving object segmentation. IEEE Transactions on Image Processing, 29, 1748–1761. https://doi.org/10.1109/TIP.2019.2945867
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REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

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

Gao, Z., Wu, Y., Harandi, M., & Jia, Y. (2020). A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3230–3244. https://doi.org/10.1109/TNNLS.2019.2939177
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Robotic and Image-Guided Knee Arthroscopy

Wu, L., Jaiprakash, A., Pandey, A. K., Fontanarosa, D., Jonmohamadi, Y., Antico, M., Strydom, M., Razjigaev, A., Sasazawa, F., Roberts, J., & Crawford, R. (2020). Robotic and Image-Guided Knee Arthroscopy. In Handbook of Robotic and Image-Guided Surgery (pp. 493–514). https://doi.org/10.1016/b978-0-12-814245-5.00029-3
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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. (2020). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12071-12078. https://doi.org/10.1609/aaai.v34i07.6885

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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. (2020). Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference. IEEE Transactions on Image Processing, 29, 277–288. https://doi.org/10.1109/TIP.2019.2928123
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One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization

Nascimento, J. C., & Carneiro, G. (2020). One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(12), 3054–3070. https://doi.org/10.1109/TPAMI.2019.2922959
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Learning Distilled Graph for Large-scale Social Network Data Clusterin

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

Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents

Law, E., Ravari, P. B., Chhibber, N., Kulic, D., Lin, S., Pantasdo, K. D., Ceha, J., Suh, S., & Dillen, N. (2020). Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents. https://doi.org/10.1145/3334480.3382783
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Book Chapters

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

Leitner, J., Morrison, D., Milan, A., Kelly-Boxall, N., McTaggart, M., Tow, A. W., & Corke, P. (2020). Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017. In Advances on Robotic Item Picking (pp. 125–148). https://doi.org/10.1007/978-3-030-35679-8_11
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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
View More
Robotic and Image-Guided Knee Arthroscopy

Wu, L., Jaiprakash, A., Pandey, A. K., Fontanarosa, D., Jonmohamadi, Y., Antico, M., Strydom, M., Razjigaev, A., Sasazawa, F., Roberts, J., & Crawford, R. (2020). Robotic and Image-Guided Knee Arthroscopy. In Handbook of Robotic and Image-Guided Surgery (pp. 493–514). https://doi.org/10.1016/b978-0-12-814245-5.00029-3
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Journal Articles

Fracture-related infection: current methods for prevention and treatment

Foster, A. L., Moriarty, T. F., Trampuz, A., Jaiprakash, A., Burch, M. A., Crawford, R., Paterson, D. L., Metsemakers, W-J., Schuetz, M., & Richards, R. G. (2020). Fracture-related infection: current methods for prevention and treatment. Expert Review of Anti-Infective Therapy, 18(4), 307–321. https://doi.org/10.1080/14787210.2020.1729740
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On the informativeness of measurements in Shiryaev’s Bayesian quickest change detectio

Ford, J. J., James, J., & Molloy, T. L. (2020). On the informativeness of measurements in Shiryaev’s Bayesian quickest change detection. Automatica, 111, 108645. https://doi.org/10.1016/j.automatica.2019.108645
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Event-based visual place recognition with ensembles of temporal windows

Fischer, T., & Milford, M. (2020). Event-based visual place recognition with ensembles of temporal windows. IEEE Robotics and Automation Letters, 5(4), 6924–6931. https://doi.org/10.1109/LRA.2020.3025505
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Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface

Fielding, A. L., Pandey, A. K., Jonmohamadi, Y., Via, R., Weber, D. C., Lomax, A. J., & Fattori, G. (2020). Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface. IEEE Sensors Journal, 1–1. https://doi.org/10.1109/jsen.2020.2993264
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Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Fernando, T., Sridharan, S., McLaren, M., Priyasad, D., Denman, S., & Fookes, C. (2020). Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection. IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 1159–1169. https://doi.org/10.1109/TASLP.2020.2982297
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Detection of Fake and Fraudulent Faces via Neural Memory Networks

Fernando, T., Fookes, C., Denman, S., & Sridharan, S. (2020). Detection of Fake and Fraudulent Faces via Neural Memory Networks. IEEE Transactions on Information Forensics and Security, 16, 1973–1988. https://doi.org/10.1109/TIFS.2020.3047768
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Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion

Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020). Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion. IEEE Signal Processing Magazine, 38(1), 87–96. https://doi.org/10.1109/MSP.2020.2988287
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From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concept

Farazi, M. R., Khan, S. H., & Barnes, N. (2020). From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concepts. Image and Vision Computing, 103, 103985. https://doi.org/10.1016/j.imavis.2020.103985
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A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Ghaemmaghami, H., & Fookes, C. (2020). A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/jbhi.2020.3027910
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Domain Generalization in Biosignal Classification

Dissanayake, T., Fernando, T., Denman, S., Ghaemmaghami, H., Sridharan, S., & Fookes, C. (2020). Domain Generalization in Biosignal Classification. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2020.3045720
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Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator

Dhanani, J., Pang, G., Pincus, J., Ahern, B., Goodwin, W., Cowling, N., Whitten, G., Abdul-Aziz, M. H., Martin, S., Corke, P., & Laupland, K. B. (2020). Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator. BMC Research Notes, 13(1), 1–6. https://doi.org/10.1186/s13104-020-05259-z
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Towards Light‐Weight Portrait Matting via Parameter Sharing

Dai, Y., Lu, H., & Shen, C. (2020). Towards Light‐Weight Portrait Matting via Parameter Sharing. Computer Graphics Forum, cgf.14179. https://doi.org/10.1111/cgf.14179
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The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry

Coory, J. A., Tan, K. G., Whitehouse, S. L., Hatton, A., Graves, S. E., & Crawford, R. W. (2020). The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(1), 132–138. https://doi.org/10.1016/j.arth.2019.08.007
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Benchmarking Simulated Robotic Manipulation through a Real World Dataset

Collins, J., McVicar, J., Wedlock, D., Brown, R., Howard, D., & Leitner, J. (2020). Benchmarking Simulated Robotic Manipulation through a Real World Dataset. IEEE Robotics and Automation Letters, 5(1), 250–257. https://doi.org/10.1109/LRA.2019.2953663
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Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry

Calabro, L., Yong, M., Whitehouse, S. L., Hatton, A., de Steiger, R., & Crawford, R. W. (2020). Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry. Journal of Arthroplasty, 35(9), 2518–2524. https://doi.org/10.1016/j.arth.2020.04.027
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Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis

Blythe, R., O’Gorman, P. M., Crawford, R. W., Feenan, R., Hatton, A., Whitehouse, S. L., & Graves, N. (2020). Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis. Journal of Arthroplasty, 35(6), 1614–1621. https://doi.org/10.1016/j.arth.2020.02.001
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Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Banach, A., Strydom, M., Jaiprakash, A., Carneiro, G., Brown, C., Crawford, R., & McFadyen, A. (2020). Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms. IEEE Access, 8, 213378–213388. https://doi.org/10.1109/ACCESS.2020.3040187
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Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors

Arthur, J. N., Chaudhry, M. U., Woodruff, M. A., Pandey, A. K., & Yambem, S. D. (2020). Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors. Advanced Electronic Materials, 6(5), 1901079. https://doi.org/10.1002/aelm.201901079
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Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy

Antico, M., Vukovic, D., Camps, S. M., Sasazawa, F., Takeda, Y., Le, A. T. H., Jaiprakash, A., Roberts, J., Crawford, R., Fontanarosa, D., & Carneiro, G. (2020). Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2543–2552. https://doi.org/10.1109/TUFFC.2020.2965291
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4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study

Antico, M., Sasazawa, F., Takeda, Y., Jaiprakash, A. T., Wille, M. L., Pandey, A. K., Crawford, R., & Fontanarosa, D. (2020). 4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study. IEEE Access, 8, 146331–146341. https://doi.org/10.1109/ACCESS.2020.3014999
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Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy

Antico, M., Sasazawa, F., Dunnhofer, M., Camps, S. M., Jaiprakash, A. T., Pandey, A. K., Crawford, R., Carneiro, G., & Fontanarosa, D. (2020). Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy. Ultrasound in Medicine and Biology, 46(2), 422–435. https://doi.org/10.1016/j.ultrasmedbio.2019.10.015
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Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery

Ali, S., Jonmohamadi, Y., Takeda, Y., Roberts, J., Crawford, R., & Pandey, A. K. (2020). Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2020.3037301
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MobileFAN: Transferring Deep Hidden Representation for Face Alignment

Zhao, Y., Liu, Y., Shen, C., Gao, Y., & Xiong, S. (2020). MobileFAN: Transferring deep hidden representation for face alignment. Pattern Recognition, 100, 107114. https://doi.org/10.1016/j.patcog.2019.107114
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Deep Multiphase Level Set for Scene Parsing

Zhang, P., Liu, W., Lei, Y., Wang, H., & Lu, H. (2020). Deep Multiphase Level Set for Scene Parsing. IEEE Transactions on Image Processing, 29, 4556–4567. https://doi.org/10.1109/TIP.2019.2957915
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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2020). A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. IEEE Transactions on Medical Imaging, 39(7), 2482–2493. https://doi.org/10.1109/TMI.2020.2972964
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Improving user specifications for robot behavior through active preference learning: Framework and evaluation

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

Lu, H., Dai, Y., Shen, C., & Xu, S. (2019, August 11). Index network. ArXiv. https://doi.org/10.1109/tpami.2020.3004474
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A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (2019, July 22). A generalized framework for edge-preserving and structure-preserving image smoothing. ArXiv, Vol. 34, pp. 11620–11628. https://doi.org/10.1609/aaai.v34i07.6830
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A Deep Journey into Super-resolution: A survey

Anwar, S., Khan, S., & Barnes, N. (2020, June 1). A Deep Journey into Super-resolution: A Survey. ACM Computing Surveys, Vol. 53, pp. 1–34. https://doi.org/10.1145/3390462
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Densely Residual Laplacian Super-Resolution

Anwar, S., & Barnes, N. (2019, June 27). Densely Residual Laplacian Super-Resolution. ArXiv. https://doi.org/10.1109/tpami.2020.3021088
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Component-based Attention for Large-scale Trademark Retrieval

Tursun, O., Denman, S., Sivapalan, S., Sridharan, S., Fookes, C., & Mau, S. (2019). Component-based Attention for Large-scale Trademark Retrieval. IEEE Transactions on Information Forensics and Security. https://doi.org/10.1109/TIFS.2019.2959921
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Towards Effective Deep Embedding for Zero-Shot Learning

Zhang, L., Wang, P., Liu, L., Shen, C., Wei, W., Zhang, Y., & Van Den Hengel, A. (2020). Towards Effective Deep Embedding for Zero-Shot Learning. IEEE Transactions on Circuits and Systems for Video Technology, 30(9), 2843–2852. https://doi.org/10.1109/TCSVT.2020.2984666
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Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

Hoang, T., Do, T. T., Le, H., Le-Tan, D. K., & Cheung, N. M. (2020). Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing. Computer Vision and Image Understanding, 191, 102852. https://doi.org/10.1016/j.cviu.2019.102852
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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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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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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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MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

Dendorfer, P., Os̆ep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L. (2021). MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking. International Journal of Computer Vision, 129(4), 845–881. https://doi.org/10.1007/s11263-020-01393-0
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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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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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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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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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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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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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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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Improving Generative Adversarial Networks with Local Coordinate Coding

Cao, J., Guo, Y., Wu, Q., Shen, C., Huang, J., & Tan, M. (2020). Improving Generative Adversarial Networks with Local Coordinate Coding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2020.3012096
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Guided Co-Segmentation Network for Fast Video Object Segmentation

Liu, W., Lin, G., Zhang, T., & Liu, Z. (2020). Guided Co-Segmentation Network for Fast Video Object Segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. https://doi.org/10.1109/tcsvt.2020.3010293
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A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots

Mao, J., Hu, X., Zhang, L., He, X., & Milford, M. (2020). A Bio-Inspired Goal-Directed Visual Navigation Model for Aerial Mobile Robots. Journal of Intelligent and Robotic Systems: Theory and Applications, 100(1), 289–310. https://doi.org/10.1007/s10846-020-01190-4
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Ground-Plane-Based Absolute Scale Estimation for Monocular Visual Odometry

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

Zaffar, M., Ehsan, S., Milford, M., & McDonald-Maier, K. D. (2020). Memorable Maps: A Framework for Re-Defining Places in Visual Place Recognition. IEEE Transactions on Intelligent Transportation Systems, 1–15. https://doi.org/10.1109/tits.2020.3001228
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A Review of Current Approaches for UAV Autonomous Mission Planning for Mars Biosignatures Detection.

Serna, J. G., Vanegas, F., Gonzalez, F., & Flannery, D. (2020). A Review of Current Approaches for UAV Autonomous Mission Planning for Mars Biosignatures Detection. 1–15. https://doi.org/10.1109/aero47225.2020.9172467
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A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs

Mandel, Nicolas & Milford, Michael & Gonzalez, Felipe. (2020). A Method for Evaluating and Selecting Suitable Hardware for Deployment of Embedded System on UAVs. Sensors. 20. 4420. 10.3390/s20164420.
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JFR special issue on agricultural robotics, part 2

Lehnert, C., McCool, C., Stachniss, C., Corke, P., Sa, I., Nieto, J., & Henten, E. J. (2020). JFR special issue on agricultural robotics, part 2. Journal of Field Robotics, 37(2), 185–186. https://doi.org/10.1002/rob.21939
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Performance improvements of a sweet pepper harvesting robot in protected cropping environments

Lehnert, C., McCool, C., Sa, I., & Perez, T. (2020). Performance improvements of a sweet pepper harvesting robot in protected cropping environments. Journal of Field Robotics, rob.21973. https://doi.org/10.1002/rob.21973
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Special issue on agricultural robotics

Lehnert, C., McCool, C., Corke, P., Sa, I., Stachniss, C., van Henten, E. J., & Nieto, J. (2020). Special issue on agricultural robotics. In Journal of Field Robotics (Vol. 37, Issue 1, pp. 5–6). John Wiley and Sons Inc. https://doi.org/10.1002/rob.21926
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What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles

Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2020). What localizes beneath: A metric multisensor localization and mapping system for autonomous underground mining vehicles. Journal of Field Robotics, rob.21978. https://doi.org/10.1002/rob.21978
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Self-Driving Vehicles: Key Technical Challenges and Progress off the Road

Milford, M., Anthony, S., & Scheirer, W. (2020). Self-Driving Vehicles: Key Technical Challenges and Progress off the Road. IEEE Potentials, 39(1), 37–45. https://doi.org/10.1109/MPOT.2019.2939376
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Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function. Computer Vision and Image Understanding, 102989. https://doi.org/10.1016/j.cviu.2020.102989
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Special Issue on Deep Learning for Robotic Vision

Angelova, A., Carneiro, G., Sünderhauf, N., & Leitner, J. (2020, May 1). Special Issue on Deep Learning for Robotic Vision. International Journal of Computer Vision. https://doi.org/10.1007/s11263-020-01324-z
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Haptics in Teleoperated Medical Interventions: Force Measurement, Haptic Interfaces and their Influence on User’s Performance

Abdi, E., Kulic, D., & Croft, E. (2020). Haptics in teleoperated medical interventions: Force measurement, haptic interfaces and their influence on users performance. IEEE Transactions on Biomedical Engineering, 1–1. https://doi.org/10.1109/tbme.2020.2987603
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Context from within: Hierarchical context modeling for semantic segmentation

Nguyen, K., Fookes, C., & Sridharan, S. (2020). Context from within: Hierarchical context modeling for semantic segmentation. Pattern Recognition, 105, 107358. https://doi.org/10.1016/j.patcog.2020.107358
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Neural Memory Plasticity for Medical Anomaly Detection

Fernando, T., Denman, S., Ahmedt-Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2020). Neural memory plasticity for medical anomaly detection. Neural Networks, 127, 67–81. https://doi.org/10.1016/j.neunet.2020.04.011
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Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses

Ahmedt Aristizabal, D., Fernando, T., Denman, S., Robinson, J. E., Sridharan, S., Johnston, P. J., Laurens, K.R., & Fookes, C. (2020). Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2020.2984238
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Bio-inspired multi-scale fusion

Hausler, S., Chen, Z., Hasselmo, M. E., & Milford, M. (2020). Bio-inspired multi-scale fusion. Biological Cybernetics, 114(2), 209–229. https://doi.org/10.1007/s00422-020-00831-z
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Counting Objects by Blockwise Classification

Liu, L., Lu, H., Xiong, H., Xian, K., Cao, Z., & Shen, C. (2020). Counting objects by blockwise classification. IEEE Transactions on Circuits and Systems for Video Technology, 30(10), 3513–3527. https://doi.org/10.1109/TCSVT.2019.2942970
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Low-cost sensors as an alternative for long-term air quality monitoring

Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S., Gao, J., Wainwright, D., Neale, D., Kan, R., Kirkwood, J., & Morawska, L. (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185, 109438. https://doi.org/10.1016/j.envres.2020.109438
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High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks

Pfister, F. M. J., Um, T. T., Pichler, D. C., Goschenhofer, J., Abedinpour, K., Lang, M., Endo, S., Ceballos-Baumann, A. O., Hirche, S., Bischl, B., Kulić, D., & Fietzek, U. M. (2020). High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks. Scientific Reports, 10(1), 5860. https://doi.org/10.1038/s41598-020-61789-3
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Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

"Gustavo Carneiro, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Alastair Burt,
Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy,
Medical Image Analysis,Volume 62,2020,101653,ISSN 1361-8415,https://doi.org/10.1016/j.media.2020.101653."
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Learning Deep Gradient Descent Optimization for Image Deconvolution

Gong, D., Zhang, Z., Shi, Q., Van Den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning Deep Gradient Descent Optimization for Image Deconvolution. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5468–5482. https://doi.org/10.1109/TNNLS.2020.2968289
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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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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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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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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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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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Deep Clustering With Sample-Assignment Invariance Prior

Peng, X., Zhu, H., Feng, J., Shen, C., Zhang, H., & Zhou, J. T. (2020). Deep Clustering with Sample-Assignment Invariance Prior. IEEE Transactions on Neural Networks and Learning Systems, 31(11), 4857–4868. https://doi.org/10.1109/TNNLS.2019.2958324
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Multiple Instance Learning with Emerging Novel Class

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

Yan, Y., Huang, Y., Chen, S., Shen, C., & Wang, H. (2020). Joint Deep Learning of Facial Expression Synthesis and Recognition. IEEE Transactions on Multimedia, 22(11), 2792–2807. https://doi.org/10.1109/TMM.2019.2962317
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Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces

Yu, X., Shiri, F., Ghanem, B., & Porikli, F. (2020). Can We See More? Joint Frontalization and Hallucination of Unaligned Tiny Faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(9), 2148–2164. https://doi.org/10.1109/TPAMI.2019.2914039
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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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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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Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Molloy, T. L., Inga, J., Flad, M., Ford, J. J., Perez, T., & Hohmann, S. (2020). Inverse open-loop noncooperative differential games and inverse optimal control. IEEE Transactions on Automatic Control, 65(2), 897–904. https://doi.org/10.1109/TAC.2019.2921835
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An Update on Retinal Prostheses

Ayton, L. N., Barnes, N., Dagnelie, G., Fujikado, T., Goetz, G., Hornig, R., Jones, B. W., Muqit, M. M. K., Rathbun, D. L., Stingl, K., Weiland, J. D., & Petoe, M. A. (2020). An update on retinal prostheses. In Clinical Neurophysiology (Vol. 131, Issue 6, pp. 1383–1398). Elsevier Ireland Ltd. https://doi.org/10.1016/j.clinph.2019.11.029
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Correlation-aware Adversarial Domain Adaptation and Generalization

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

Fernando, T., Ghaemmaghami, H., Denman, S., Sridharan, S., Hussain, N., & Fookes, C. (2020). Heart Sound Segmentation Using Bidirectional LSTMs with Attention. IEEE Journal of Biomedical and Health Informatics, 24(6), 1601–1609. https://doi.org/10.1109/JBHI.2019.2949516
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REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

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

Gao, Z., Wu, Y., Harandi, M., & Jia, Y. (2020). A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3230–3244. https://doi.org/10.1109/TNNLS.2019.2939177
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One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization

Nascimento, J. C., & Carneiro, G. (2020). One Shot Segmentation: Unifying Rigid Detection and Non-Rigid Segmentation Using Elastic Regularization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(12), 3054–3070. https://doi.org/10.1109/TPAMI.2019.2922959
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Learning Distilled Graph for Large-scale Social Network Data Clusterin

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

Why are Generative Adversarial Networks so Fascinating and Annoying?

Faria, F. A., & Carneiro, G. (2020). Why are Generative Adversarial Networks so Fascinating and Annoying? Proceedings - 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2020, 1–8. https://doi.org/10.1109/SIBGRAPI51738.2020.00009
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Learning Object Relation Graph and Tentative Policy for Visual Navigation

Du H., Yu X., Zheng L. (2020) Learning Object Relation Graph and Tentative Policy for Visual Navigation. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_2
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Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path

Deremetz, M., Lenain, R., Laneurit, J., Debain, C., & Peynot, T. (2020). Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path. CCTA 2020 - 4th IEEE Conference on Control Technology and Applications, 372–379. https://doi.org/10.1109/CCTA41146.2020.9206153
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Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n

Dellaert F., Rosen D.M., Wu J., Mahony R., Carlone L. (2020) Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_18
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A robotic vision system for inspection of soiling at CSP plants

Coventry, J., Asselineau, C. A., Salahat, E., Raman, M. A., & Mahony, R. (2020). A robotic vision system for inspection of soiling at CSP plants. AIP Conference Proceedings, 2303(1), 100001. https://doi.org/10.1063/5.0029493
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Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution

Chan, W. P., Sakr, M., Quintero, C. P., Croft, E., & Van Der Loos, H. F. M. H. (2020). Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution. 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, 419–424. https://doi.org/10.1109/RO-MAN47096.2020.9223526
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Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization

Campbell D., Liu L., Gould S. (2020) Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12347. Springer, Cham. https://doi.org/10.1007/978-3-030-58536-5_15
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A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps

Cagara, D., Dunbabin, M., & Rigby, P. (2020). A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps. Proceedings - IEEE International Conference on Robotics and Automation, 8573–8579. https://doi.org/10.1109/ICRA40945.2020.9196680
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Neural Memory Networks for Seizure Type Classification

Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M. J., & Fookes, C. (2020). Neural Memory Networks for Seizure Type Classification. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 569–575. https://doi.org/10.1109/EMBC44109.2020.9175641
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Attention Networks for Multi-Task Signal Analysis

Ahmedt-Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2020). Attention Networks for Multi-Task Signal Analysis. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020-July, 184–187. https://doi.org/10.1109/EMBC44109.2020.9175730
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Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising

Zhang, H., Li, Y., Chen, H., & Shen, C. (2020). Memory-efficient hierarchical neural architecture search for image denoising. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3654–3663. https://doi.org/10.1109/CVPR42600.2020.00371
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Visual Odometry Revisited: What Should Be Learnt?

Zhan, H., Weerasekera, C. S., Bian, J. W., & Reid, I. (2020). Visual Odometry Revisited: What Should Be Learnt? Proceedings - IEEE International Conference on Robotics and Automation, 4203–4210. https://doi.org/10.1109/ICRA40945.2020.9197374
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PolarMask: Single Shot Instance Segmentation with Polar Representation

Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., & Luo, P. (2020). PolarMask: Single shot instance segmentation with polar representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12190–12199. https://doi.org/10.1109/CVPR42600.2020.01221
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SOLO: Segmenting Objects by Locations

Wang X., Kong T., Shen C., Jiang Y., Li L. (2020) SOLO: Segmenting Objects by Locations. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12363. Springer, Cham. https://doi.org/10.1007/978-3-030-58523-5_38
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Optimal Feature Transport for Cross-View Image Geo-Localization

Shi, Y., Yu, X., Liu, L., Zhang, T., & Li, H. (2020). Optimal Feature Transport for Cross-View Image Geo-Localization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11990-11997. https://doi.org/10.1609/aaai.v34i07.6875
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Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

Nguyen, C., Do, T. T., & Carneiro, G. (2020). Uncertainty in model-agnostic meta-learning using variational inference. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 3079–3089. https://doi.org/10.1109/WACV45572.2020.9093536
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Unsupervised Task Design to Meta-Train Medical Image Classifiers

Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2020). Unsupervised Task Design to Meta-Train Medical Image Classifiers. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1339–1342. https://doi.org/10.1109/ISBI45749.2020.9098470
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Photoshopping Colonoscopy Video Frames

Liu, Y., Tian, Y., Maicas, G., Cheng Tao Pu, L. Z., Singh, R., Verjans, J. W., & Carneiro, G. (2020). Photoshopping Colonoscopy Video Frames. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1642–1646. https://doi.org/10.1109/ISBI45749.2020.9098406
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Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Li, D., Opazo, C. R., Yu, X., & Li, H. (2020). Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1448–1458. https://doi.org/10.1109/WACV45572.2020.9093512
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Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2020). Semi-Supervised Multi-Domain Multi-Task Training for Metastatic Colon Lymph Node Diagnosis from Abdominal CT. Proceedings - International Symposium on Biomedical Imaging, 2020-April, 1478–1481. https://doi.org/10.1109/ISBI45749.2020.9098372
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End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

Chen, B., Parra, Á., Cao, J., Li, N., & Chin, T. J. (2020). End-to-end learnable geometric vision by backpropagating PNP optimization. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8097–8106. https://doi.org/10.1109/CVPR42600.2020.00812
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CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

Chancan, M., & Milford, M. (2020). CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning. Proceedings - IEEE International Conference on Robotics and Automation, 1697–1704. https://doi.org/10.1109/ICRA40945.2020.9197336
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Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraint

Ch’ng, S. F., Sogi, N., Purkait, P., Chin, T. J., & Fukui, K. (2020). Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints∗. Proceedings - IEEE International Conference on Robotics and Automation, 9680–9686. https://doi.org/10.1109/ICRA40945.2020.9196902
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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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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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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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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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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. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020-June, 1514–1522. https://doi.org/10.1109/CVPRW50498.2020.00192
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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. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1873–1882. https://doi.org/10.1109/WACV45572.2020.9093589
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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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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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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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Anomaly Detection via Neighbourhood Contrast. Lecture Notes in Computer Science

Chen B., Ting K.M., Chin TJ. (2020) Anomaly Detection via Neighbourhood Contrast. In: Lauw H., Wong RW., Ntoulas A., Lim EP., Ng SK., Pan S. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2020. Lecture Notes in Computer Science, vol 12085. Springer, Cham. https://doi.org/10.1007/978-3-030-47436-2_49
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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. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12354. Springer, Cham. https://doi.org/10.1007/978-3-030-58545-7_11
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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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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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Towards Simulating Semantic Onboard UAV Navigation

Mandel, N., Alvarez, F. V., Milford, M., & Gonzalez, F. (2020, March 1). Towards Simulating Semantic Onboard UAV Navigation. IEEE Aerospace Conference Proceedings. https://doi.org/10.1109/AERO47225.2020.9172771
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Fast postprocessing for difficult discrete energy minimization problems

Akhter, I., Cheong, L. F., & Hartley, R. (2020). Fast postprocessing for difficult discrete energy minimization problems. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 3462–3471).
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A Stochastic Conditioning Scheme for Diverse Human Motion Prediction

Aliakbarian, S., Sadat, F., †1, S., Salzmann, M., Petersson, L., & Gould, S. (2020). A Stochastic Conditioning Scheme for Diverse Human Motion Prediction *. In The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 5223–5232).
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Blended convolution and synthesis for efficient discrimination of 3D shapes

Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2020). Blended convolution and synthesis for efficient discrimination of 3D shapes. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 21–31).
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Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution

Song, X., Dai, Y., Zhou, D., Liu, L., Li, W., Li, H., & Yang, R. (2020). Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
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Geometry to the Rescue : 3D Instance Reconstruction from a Cluttered Scene

Li, L., & Barnes, N. (2020). Geometry to the Rescue : 3D Instance Reconstruction from a Cluttered Scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
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Proposal-free temporal moment localization of a natural-language query in video using guided attention

Rodriguez-Opazo, C., Marrese-Taylor, E., Saleh, F. S., Li, H., & Gould, S. (2020). Proposal-free temporal moment localization of a natural-language query in video using guided attention. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 2453–2462. https://doi.org/10.1109/WACV45572.2020.9093328
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Robust Ego and Object 6-DoF Motion Estimation and Tracking

Zhang, J., Henein, M., Mahony, R., & Viorela, I. (2020). Robust Ego and Object 6-DoF Motion Estimation and Tracking. IROS 2020.
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Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

Yang, J., Mao, W., Alvarez, J. M., & Liu, M. (2020). Cost volume pyramid based depth inference for multi-view stereo. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4876–4885. https://doi.org/10.1109/CVPR42600.2020.00493
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From Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction

Lu, K., Barnes, N., Anwar, S., & Zheng, L. (2020). From depth what can you see? Depth completion via auxiliary image reconstruction. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 11303–11312. https://doi.org/10.1109/CVPR42600.2020.01132
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Learning Landmark Guided Embeddings for Animal Re-identification

Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning Landmark Guided Embeddings for Animal Re-identification. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2020, 12–19. https://doi.org/10.1109/WACVW50321.2020.9096932
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Probabilistic object detection: Definition and evaluation

Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., Carneiro, G., Angelova, A., & Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, 1020–1029. https://doi.org/10.1109/WACV45572.2020.9093599
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Autonomous UAV Navigation for Active Perception of Targets in Uncertain and Cluttered Environments

Sandino, J., Vanegas, F., Gonzalez, F., & Maire, F. (2020). Autonomous UAV Navigation for active perception of targets in uncertain and cluttered environments. Proceedings of 2020 IEEE Aerospace Conference.
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Attention Driven Fusion for Multi-Modal Emotion Recognition

Priyasad, D., Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020, April 9). Attention Driven Fusion for Multi-Modal Emotion Recognition. 3227–3231. https://doi.org/10.1109/icassp40776.2020.9054441
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DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares

Ben-Shabat Y., Gould S. (2020) DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12346. Springer, Cham. https://doi.org/10.1007/978-3-030-58452-8_2
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On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering

Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., Chan, C. S., van den Hengel, A., & Wang, L. (2020). On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering. Retrieved from http://arxiv.org/abs/2002.10215
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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. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 8570–8578. https://doi.org/10.1109/CVPR42600.2020.00860
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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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OpenGAN: Open Set Generative Adversarial Networks

Ditria L., Meyer B.J., Drummond T. (2021) OpenGAN: Open Set Generative Adversarial Networks. In: Ishikawa H., Liu CL., Pajdla T., Shi J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science, vol 12625. Springer, Cham. https://doi.org/10.1007/978-3-030-69538-5_29
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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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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation

Pan, L., Dai, Y., Liu, M., Porikli, F., & Pan, Q. (2020). Joint stereo video deblurring, scene flow estimation and moving object segmentation. IEEE Transactions on Image Processing, 29, 1748–1761. https://doi.org/10.1109/TIP.2019.2945867
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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. (2020). V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12071-12078. https://doi.org/10.1609/aaai.v34i07.6885

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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. (2020). Model-Free Tracker for Multiple Objects Using Joint Appearance and Motion Inference. IEEE Transactions on Image Processing, 29, 277–288. https://doi.org/10.1109/TIP.2019.2928123
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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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Edited Collection

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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Guest Editorial: Special Issue on ACCV 2018

Jawahar, C. V., Li, H., Mori, G., & Schindler, K. (2020). Guest Editorial: Special Issue on ACCV 2018. International Journal of Computer Vision, 128(4), 909.
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Submitted

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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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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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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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

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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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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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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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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

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Joint Estimation of Expertise and Reward Preferences From Human Demonstrations

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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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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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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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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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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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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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Visual Question Answering with Prior Class Semantics

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

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

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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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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

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

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

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AQD: Towards Accurate Quantized Object Detection

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Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

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Soft Expert Reward Learning for Vision-and-Language Navigation

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Object-and-Action Aware Model for Visual Language Navigation

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AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

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How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?

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

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Single Image Optical Flow Estimation with an Event Camera

Pan, L., Liu, M., & Hartley, R. (2020). Single Image Optical Flow Estimation with an Event Camera, CVPR 2020. http://arxiv.org/abs/2004.00347
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Rethinking Class Relations: Absolute-relative Few-shot Learning

Zhang, H., Torr, P. H. S., Li, H., Jian, S., & Koniusz, P. (2020). Rethinking Class Relations: Absolute-relative Few-shot Learning, 1–10.
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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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VDO-SLAM: A Visual Dynamic Object-aware SLAM System

Zhang, J., Henein, M., Mahony, R., & Ila, V. (2020). VDO-SLAM: A Visual Dynamic Object-aware SLAM System.
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A Signal Propagation Perspective for Pruning Neural Networks at initialization

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Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision

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Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing

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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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VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

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Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons

Molloy, T. L., Ford, J. J., & Perez, T. (2020). Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons. Proceedings of the IEEE Conference on Decision and Control, 2018-December, 1663–1668. Retrieved from http://arxiv.org/abs/2005.06153
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Active Preference Learning using Maximum Regret

Wilde, N., Kulic, D., & Smith, S. L. (2020). Active Preference Learning using Maximum Regret. http://arxiv.org/abs/2005.04067
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Supportive Actions for Manipulation in Human-Robot Coworker Teams

Bansal, S., Newbury, R., Chan, W., Cosgun, A., Allen, A., Kulić, D., Drummond, D., & Isbell, C. (2020). Supportive Actions for Manipulation in Human-Robot Coworker Teams. Retrieved from http://arxiv.org/abs/2005.00769
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End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification. Retrieved from http://arxiv.org/abs/2005.03222
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Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition

Nguyen, D., Nguyen, D. T., Zeng, R., Nguyen, T. T., Tran, S. N., Nguyen, T., Sridharan, S., & Fookes, C. (2020). Deep Auto-Encoders with Sequential Learning for Multimodal Dimensional Emotion Recognition.
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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

Newbury, R., He, K., Cosgun, A., & Drummond, T. (2020). Learning to Place Objects onto Flat Surfaces in Human-Preferred Orientations. Retrieved from http://arxiv.org/abs/2004.00249
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Joint Deep Cross-Domain Transfer Learning for Emotion Recognition

Nguyen, D., Sridharan, S., Nguyen, D. T., Denman, S., Tran, S. N., Zeng, R., & Fookes, C. (2020). Joint Deep Cross-Domain Transfer Learning for Emotion Recognition. (2018). Retrieved from http://arxiv.org/abs/2003.11136
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How to Train Your Event Camera Neural Network

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Generative Low-bitwidth Data Free Quantization

Xu, S., Li, H., Zhuang, B., Liu, J., Cao, J., Liang, C., & Tan, M. (2020). Generative Low-bitwidth Data Free Quantization. Retrieved from http://arxiv.org/abs/2003.03603
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Efficient Semantic Video Segmentation with Per-frame Inference

Liu, Y., Shen, C., Yu, C., & Wang, J. (2020). Efficient Semantic Video Segmentation with Per-frame Inference. Retrieved from https://tinyurl.com/segment-video
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PAC-Bayesian Meta-learning with Implicit Prior

Nguyen, C., Do, T.-T., & Carneiro, G. (2020). PAC-Bayesian Meta-learning with Implicit Prior. Retrieved from http://arxiv.org/abs/2003.02455
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ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

Liu, Y., Chen, H., Shen, C., He, T., Jin, L., & Wang, L. (2020). ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network. Retrieved from http://arxiv.org/abs/2002.10200
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Globally Optimal Contrast Maximisation for Event-based Motion Estimation

Liu, D., Parra, Á., & Chin, T.-J. (2020). Globally Optimal Contrast Maximisation for Event-based Motion Estimation. Retrieved from http://arxiv.org/abs/2002.10686
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3D Gated Recurrent Fusion for Semantic Scene Completion

Liu, Y., Li, J., Yan, Q., Yuan, X., Zhao, C., Reid, I., & Cadena, C. (2020). 3D Gated Recurrent Fusion for Semantic Scene Completion. Retrieved from http://arxiv.org/abs/2002.07269
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Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning

Zhang, H., Liu, Y., Fang, B., Li, Y., Liu, L., & Reid, I. (2020). Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning. Retrieved from https://github.com/UniLauX/AINet
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DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

Yin, W., Wang, X., Shen, C., Liu, Y., Tian, Z., Xu, S., Sun, C., & Renyin, D. (2020). DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data. Retrieved from http://arxiv.org/abs/2002.00569
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Learn to Predict Sets Using Feed-Forward Neural Networks

Rezatofighi, H., Kaskman, R., Motlagh, F. T., Shi, Q., Milan, A., Cremers, D., Leal-Taixé, L., & Reid, I. (2020). Learn to Predict Sets Using Feed-Forward Neural Networks. Retrieved from http://arxiv.org/abs/2001.11845
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Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

Luo, C., Lin, Q., Liu, Y., Jin, L., & Shen, C. (2020). Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild. Retrieved from http://arxiv.org/abs/2001.04189
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Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory

Zhang, X., Gong, D., Cao, J., & Shen, C. (2020). Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory. Retrieved from http://arxiv.org/abs/2001.04123
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From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting

Xiong, H., Lu, H., Liu, C., Liu, L., Shen, C., & Cao, Z. (2020). From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting. Retrieved from https://tinyurl.com/SS-DCNet.
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Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation

He, T., Gong, D., Tian, Z., & Shen, C. (2020). 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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Switchable Precision Neural Networks

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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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String stable integral control of vehicle platoons with disturbances

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A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

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Enhancing Feature Invariance with Learned Image Transformations for Image Retrieval

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

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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

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

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

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Hierarchical Multi-Process Fusion for Visual Place Recognition

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

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

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Lecture Notes

Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps

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

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

Pairwise Relation Learning for Semi-supervised Gland Segmentation

Book Section

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

Scientific Publications

Fracture-related infection: current methods for prevention and treatment

On the informativeness of measurements in Shiryaev’s Bayesian quickest change detectio

Event-based visual place recognition with ensembles of temporal windows

Preliminary study of the Intel RealSenseTM D415 camera for monitoring respiratory like motion of an irregular surface

Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection

Detection of Fake and Fraudulent Faces via Neural Memory Networks

Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion

Why are Generative Adversarial Networks so Fascinating and Annoying?

From known to the unknown: Transferring knowledge to answer questions about novel visual and semantic concept

A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation

Domain Generalization in Biosignal Classification

Learning Object Relation Graph and Tentative Policy for Visual Navigation

Increasing ventilator surge capacity in COVID 19 pandemic: Design, manufacture and in vitro-in vivo testing in anaesthetized healthy pigs of a rapid prototyped mechanical ventilator

Autonomous human tracking using uwb sensors for mobile robots: An observer-based approach to follow the human path

Towards Light‐Weight Portrait Matting via Parameter Sharing

Shonan Rotation Averaging: Global Optimality by Surfing SO(p)n

The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry

Benchmarking Simulated Robotic Manipulation through a Real World Dataset

A robotic vision system for inspection of soiling at CSP plants

Towards a Multimodal System combining Augmented Reality and Electromyography for Robot Trajectory Programming and Execution

Mortality and Implant Survival With Simultaneous and Staged Bilateral Total Hip Arthroplasty: Experience From the Australian Orthopedic Association National Joint Replacement Registry

Solving the Blind Perspective-n-Point Problem End-to-End with Robust Differentiable Geometric Optimization

A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps

Fixation Method for Hip Arthroplasty Stem Following Hip Fracture: A Population-Level Cost-Effectiveness Analysis

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Effect of Gate Conductance on Hygroscopic Insulator Organic Field‐Effect Transistors

Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy

4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study

Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy

Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery

Neural Memory Networks for Seizure Type Classification

Attention Networks for Multi-Task Signal Analysis

MobileFAN: Transferring Deep Hidden Representation for Face Alignment

Deep Multiphase Level Set for Scene Parsing

Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising

Visual Odometry Revisited: What Should Be Learnt?

A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

PolarMask: Single Shot Instance Segmentation with Polar Representation

Improving user specifications for robot behavior through active preference learning: Framework and evaluation

SOLO: Segmenting Objects by Locations

Optimal Feature Transport for Cross-View Image Geo-Localization

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

Unsupervised Task Design to Meta-Train Medical Image Classifiers

Index Network

Photoshopping Colonoscopy Video Frames

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization

CityLearn: Diverse Real-World Environments for Sample-Efficient Navigation Policy Learning

Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraint

A Deep Journey into Super-resolution: A survey

Densely Residual Laplacian Super-Resolution

Component-based Attention for Large-scale Trademark Retrieval

Towards Effective Deep Embedding for Zero-Shot Learning

Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

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

Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization

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

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

Sub-Instruction Aware Vision-and-Language Navigation

ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

Dynamic SLAM: The Need For Speed

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

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

Random Erasing Data Augmentation

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

A Minimum Energy Filter for Localisation of an Unmanned Aerial Vehicle

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

Bidirectional Self-Normalizing Neural Networks

Calibration of Neural Networks using Splines