2018 Publications

Scientific Publications

  • Learning to Predict Crisp Boundaries

    Deng R., Shen C., Liu S., Wang H., Liu X. (2018) Learning to Predict Crisp Boundaries. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11210. Springer, Cham. https://doi.org/10.1007/978-3-030-01231-1_35

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  • Robust Fitting in Computer Vision: Easy or Hard?

    Chin TJ., Cai Z., Neumann F. (2018) Robust Fitting in Computer Vision: Easy or Hard?. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham. https://doi.org/10.1007/978-3-030-01258-8_43

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  • Deterministic Consensus Maximization with Biconvex Programming

    Cai Z., Chin TJ., Le H., Suter D. (2018) Deterministic Consensus Maximization with Biconvex Programming. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham. https://doi.org/10.1007/978-3-030-01258-8_42

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  • A Binary Optimization Approach for Constrained K-Means Clustering

    Le H.M., Eriksson A., Do TT., Milford M. (2019) A Binary Optimization Approach for Constrained K-Means Clustering. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_24

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  • Traversing Latent Space using Decision Ferns

    Zuo Y., Avraham G., Drummond T. (2019) Traversing Latent Space Using Decision Ferns. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_37

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  • Stereo Computation for a Single Mixture Image

    Zhong Y., Dai Y., Li H. (2018) Stereo Computation for a Single Mixture Image. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11213. Springer, Cham. https://doi.org/10.1007/978-3-030-01240-3_2

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  • Learning Free-Form Deformations for 3D Object Reconstruction

    Jack, D., Pontes, J. K., Sridharan, S., Fookes, C., Shirazi, S., Maire, F., & Eriksson, A. (2019). Learning Free-Form Deformations for 3D Object Reconstruction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11362 LNCS, 317–333. https://doi.org/10.1007/978-3-030-20890-5_21

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  • Monocular Depth Estimation with Augmented Ordinal Depth Relationships

    Cao, Y., Zhao, T., Xian, K., Shen, C., Cao, Z., & Xu, S. (2018). Monocular Depth Estimation with Augmented Ordinal Depth Relationships. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2018.2877944

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  • Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

    Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M. (2018). Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 340–349). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00043

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  • Discrimination-aware channel pruning for deep neural networks

    Zhuang, Z., Tan, M., Zhuang, B., Liu, J., Guo, Y., Wu, Q., Huang, J., & Zhu, J. (2018). Discrimination-aware Channel Pruning for Deep Neural Networks. Advances in Neural Information Processing Systems, 2018-December, 875–886.

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  • OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions

    Talbot, B., Garg, S., & Milford, M. (2018). OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7758–7765). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593761

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  • Scalable Deep k-Subspace Clustering

    Zhang T., Ji P., Harandi M., Hartley R., Reid I. (2019) Scalable Deep k-Subspace Clustering. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_30

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  • Continuous-Time Intensity Estimation Using Event Cameras

    Scheerlinck C., Barnes N., Mahony R. (2019) Continuous-Time Intensity Estimation Using Event Cameras. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_20

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  • Action Anticipation by Predicting Future Dynamic Images

    Rodriguez C., Fernando B., Li H. (2019) Action Anticipation by Predicting Future Dynamic Images. In: Leal-Taixé L., Roth S. (eds) Computer Vision – ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science, vol 11131. Springer, Cham. https://doi.org/10.1007/978-3-030-11015-4_10

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  • An adaptive localization system for image storage and localization latency requirements

    Mao, J., Hu, X., & Milford, M. (2018). An adaptive localization system for image storage and localization latency requirements. Robotics and Autonomous Systems, 107, 246–261. http://doi.org/10.1016/J.ROBOT.2018.06.007

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  • Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles

    *McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5719–5726). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593529

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  • Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

    Morrison, D., Corke, P., & Leitner, J. (2018). Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach. Retrieved from http://arxiv.org/abs/1804.05172

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  • Efficient Subpixel Refinement with Symbolic Linear Predictors

    Lui, V., Geeves, J., Yii, W., & Drummond, T. (2018). Efficient Subpixel Refinement with Symbolic Linear Predictors. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8165–8173). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00852

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  • Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection

    James, J., Ford, J. J., & Molloy, T. L. (2018). Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection. IEEE Transactions on Control Systems Technology, 1–8. http://doi.org/10.1109/TCST.2018.2872468

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  • Structure Aware SLAM Using Quadrics and Planes

    Hosseinzadeh M., Latif Y., Pham T., Suenderhauf N., Reid I. (2019) Structure Aware SLAM Using Quadrics and Planes. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_26

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  • Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

    Hausler, S., Jacobson, A., & Milford, M. (2018). Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration. Retrieved from http://arxiv.org/abs/1810.12465

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  • LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

    Garg, S., Suenderhauf, N., & Milford, M. (2018). LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics. Retrieved from http://arxiv.org/abs/1804.05526

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  • An End-to-End TextSpotter with Explicit Alignment and Attention

    He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., & Sun, C. (2018). An End-to-End TextSpotter with Explicit Alignment and Attention. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5020–5029). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00527

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  • Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

    Garg, S., Suenderhauf, N., & Milford, M. (2018). Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3645–3652). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8461051

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  • ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

    Dharmasiri T., Spek A., Drummond T. (2019) ENG: End-to-End Neural Geometry for Robust Depth and Pose Estimation Using CNNs. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_39

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  • Neural Algebra of Classifiers

    *Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2018). Neural Algebra of Classifiers. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 729–737). IEEE. https://doi.org/10.1109/WACV.2018.00085

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  • Towards vision-based manipulation of plastic materials

    *Cherubini, A., Leitner, J., Ortenzi, V., & Corke, P. (2018). Towards vision-based manipulation of plastic materials. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 485–490). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594108

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  • Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

    Campbell, D. J., Petersson, L., Kneip, L., & Li, H. (2018). Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2018.2848650

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  • Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

    Bruce, J., Sünderhauf, N., Mirowski, P., Hadsell, R., & Milford, M. (2018). Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal. Retrieved from http://arxiv.org/abs/1807.05211

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  • Training Deep Neural Networks for Visual Servoing

    *Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., & Corke, P. (2018). Training Deep Neural Networks for Visual Servoing. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–8). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8461068

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  • VIENA 2: A Driving Anticipation Dataset

    Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2019). VIENA2: A Driving Anticipation Dataset. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11361 LNCS, 449–466. https://doi.org/10.1007/978-3-030-20887-5_28

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  • Model-free and learning-free grasping by Local Contact Moment matching

    *Adjigble, M., Marturi, N., Ortenzi, V., Rajasekaran, V., Corke, P., & Stolkin, R. (2018). Model-free and learning-free grasping by Local Contact Moment matching. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2933–2940). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594226

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  • Dense 3D Face Correspondence

    Gilani, S. Z., Mian, A., Shafait, F., & Reid, I. (2018). Dense 3D Face Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7), 1584–1598. https://doi.org/10.1109/TPAMI.2017.2725279

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  • Coresets for Triangulation

    Gilani, S. Z., Mian, A., Shafait, F., & Reid, I. (2018). Dense 3D Face Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7), 1584–1598. https://doi.org/10.1109/TPAMI.2017.2725279

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  • The Role of Symmetry in Rigidity Analysis: A Tool for Network Localisation and Formation Control

    Stacey, G., & Mahony, R. (2018). The Role of Symmetry in Rigidity Analysis: A Tool for Network Localization and Formation Control. IEEE Transactions on Automatic Control, 63(5), 1313–1328. https://doi.org/10.1109/TAC.2017.2747760

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  • Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

    Saleh, F. S., Aliakbarian, M. S., Salzmann, M., Petersson, L., Alvarez, J. M., & Gould, S. (2018). Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1382–1396. https://doi.org/10.1109/TPAMI.2017.2713785

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  • Guaranteed Outlier Removal for Point Cloud Registration with Correspondences

    Guaranteed Outlier Removal for Point Cloud Registration with Correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2868–2882. https://doi.org/10.1109/TPAMI.2017.2773482

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  • Structured Learning of Tree Potentials in CRF for Image Segmentation

    Liu, F., Lin, G., Qiao, R., & Shen, C. (2017). Structured Learning of Tree Potentials in CRF for Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1–7. http://doi.org/10.1109/TNNLS.2017.2690453 *In Press

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  • Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies

    Jacobson, A., Scheirer, W., & Milford, M. (2017). Déjà vu: Scalable place recognition using mutually supportive feature frequencies. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 6654–6661. https://doi.org/10.1109/IROS.2017.8206580

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  • Design of a multi-modal end-effector and grasping system- How integrated design helped win the Amazon Robotics Challenge

    Kelly-Boxall, N., Morrison, D., Wade-McCue, S., Corke, P., & Leitner, J. (2018). Design of a multi-modal end-effector and grasping system- How integrated design helped win the amazon robotics challenge. Australasian Conference on Robotics and Automation, ACRA, 2018-December.

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  • Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

    Harandi, M., Salzmann, M., & Hartley, R. (2018). Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1), 48–62. https://doi.org/10.1109/TPAMI.2017.2655048

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  • On the structure of kinematic systems with complete symmetry

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

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  • Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

    Yang J., Gong D., Liu L., Shi Q. (2018) Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11207. Springer, Cham. https://doi.org/10.1007/978-3-030-01219-9_40

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  • Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge

    Teney, D., Anderson, P., He, X., & Hengel, A. van den. (2018). Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4223–4232). IEEE. http://doi.org/10.1109/CVPR.2018.00444

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  • Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

    Zhu, G., Porikli, F., & Li, H. (2018). Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, 28(2), 314–326. http://doi.org/10.1109/TCSVT.2016.2615518

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  • Deblurring Natural Image Using Super-Gaussian Fields

    Liu Y., Dong W., Gong D., Zhang L., Shi Q. (2018) Deblurring Natural Image Using Super-Gaussian Fields. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11205. Springer, Cham. https://doi.org/10.1007/978-3-030-01246-5_28

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  • Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation

    Khan, S. H., Hayat, M., & Barnes, N. (2018). Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1312–1320). Lake Tahoe, United States: IEEE. https://doi.org/10.1109/WACV.2018.00148

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  • Semi-dense 3D Reconstruction with a Stereo Event Camera

    Zhou Y., Gallego G., Rebecq H., Kneip L., Li H., Scaramuzza D. (2018) Semi-dense 3D Reconstruction with a Stereo Event Camera. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11205. Springer, Cham. https://doi.org/10.1007/978-3-030-01246-5_15

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  • 3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

    *Zhong, Y., Dai, Y., & Li, H. (2018). 3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 2343–2349). IEEE. http://doi.org/10.1109/ICPR.2018.8545378

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  • Open-World Stereo Video Matching with Deep RNN

    Zhong Y., Li H., Dai Y. (2018) Open-World Stereo Video Matching with Deep RNN. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11206. Springer, Cham. https://doi.org/10.1007/978-3-030-01216-8_7

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  • Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

    *Zhang, J., Zhang, T., Daf, Y., Harandi, M., & Hartley, R. (2018). Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9029–9038). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00941

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  • Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables

    Varamin, A. A., Abbasnejad, E., Shi, Q., Ranasinghe, D. C., & Rezatofighi, H. (2018). Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables (Vol. 18). Retrieved from https://doi.org/10.475/123_4

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  • Robust Visual Odometry in Underwater Environment

    *Zhang, J., Ila, V., & Kneip, L. (2018). Robust Visual Odometry in Underwater Environment. In 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) (pp. 1–9). Kobe, Japan: IEEE. http://doi.org/10.1109/OCEANSKOBE.2018.8559452

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  • Goal-Oriented Visual Question Generation via Intermediate Rewards

    Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J., & van den Hengel, A. (2018). Goal-Oriented Visual Question Generation via Intermediate Rewards. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11209 LNCS, 189–204. https://doi.org/10.1007/978-3-030-01228-1_12

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

    *Yu, X., Fernando, B., Hartley, R., & Porikli, F. (2018). Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 908–917). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00101

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  • Face Super-Resolution Guided by Facial Component Heatmaps

    Yu X., Fernando B., Ghanem B., Porikli F., Hartley R. (2018) Face Super-Resolution Guided by Facial Component Heatmaps. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11213. Springer, Cham. https://doi.org/10.1007/978-3-030-01240-3_14

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  • Learning Discriminative Video Representations Using Adversarial Perturbations

    Wang J., Cherian A. (2018) Learning Discriminative Video Representations Using Adversarial Perturbations. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11208. Springer, Cham. https://doi.org/10.1007/978-3-030-01225-0_42

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  • Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

    Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2018). Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. Journal of Digital Imaging, 31(6), 869–878. http://doi.org/10.1007/s10278-018-0084-9

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  • Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing

    Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2018). Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. http://doi.org/10.1109/TCSVT.2018.2890202

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  • Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments

    Liu, L., Li, H., Dai, Y., & Pan, Q. (2018). Robust and efficient relative pose with a Multi-Camera system for autonomous driving in highly dynamic environments. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2432–2444. https://doi.org/10.1109/TITS.2017.2749409

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  • Reading car license plates using deep neural networks

    Li, H., Wang, P., You, M., & Shen, C. (2018). Reading car license plates using deep neural networks. Image and Vision Computing, 72, 14–23. http://doi.org/10.1016/J.IMAVIS.2018.02.002

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  • Structure from Recurrent Motion: From Rigidity to Recurrency

    *Li, X., Li, H., Joo, H., Liu, Y., & Sheikh, Y. (2018). Structure from Recurrent Motion: From Rigidity to Recurrency. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3032–3040). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00320

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  • Kernel Support Vector Machines and Convolutional Neural Networks

    Jiang, S., Hartley, R., & Fernando, B. (2018). Kernel Support Vector Machines and Convolutional Neural Networks. In 2018 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1–7). Canberra, Australia: IEEE. http://doi.org/10.1109/DICTA.2018.8615840

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  • Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking

    Jacobson, A., Zeng, F., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2018). Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3970–3977). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593750

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  • Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries

    Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. van den. (2018). Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4252–4261). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00447

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  • Towards Effective Low-Bitwidth Convolutional Neural Networks

    Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2018). Towards Effective Low-Bitwidth Convolutional Neural Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7920–7928). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00826

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  • Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning

    Wu, Q., Wang, P., Shen, C., Reid, I., & Hengel, A. van den. (2018). Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6106–6115). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00639

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  • Bayesian Semantic Instance Segmentation in Open Set World

    Pham, T., Vijay Kumar, B. G., Do, T. T., Carneiro, G., & Reid, I. (2018). Bayesian semantic instance segmentation in open set world. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11214 LNCS, 3–18. https://doi.org/10.1007/978-3-030-01249-6_1

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  • Training Medical Image Analysis Systems like Radiologists

    Maicas G., Bradley A.P., Nascimento J.C., Reid I., Carneiro G. (2018) Training Medical Image Analysis Systems like Radiologists. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_62

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  • Visual Question Answering with Memory-Augmented Networks

    Ma, C., Shen, C., Dick, A., Wu, Q., Wang, P., Hengel, A. van den, & Reid, I. (2018). Visual Question Answering with Memory-Augmented Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6975–6984). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00729

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  • Deep Regression Tracking with Shrinkage Loss

    Lu X., Ma C., Ni B., Yang X., Reid I., Yang MH. (2018) Deep Regression Tracking with Shrinkage Loss. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11218. Springer, Cham. https://doi.org/10.1007/978-3-030-01264-9_22

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  • Exploring Context with Deep Structured Models for Semantic Segmentation

    Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2018). Exploring Context with Deep Structured Models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352–1366. http://doi.org/10.1109/TPAMI.2017.2708714

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  • Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields

    Li K., Pham T., Zhan H., Reid I. (2018) Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham. https://doi.org/10.1007/978-3-030-01258-8_31

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  • Drones count wildlife more accurately and precisely than humans

    Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D., Ramesh, R.S., Reid, I., Terauds, A., & Koh, L. P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160–1167. http://doi.org/10.1111/2041-210X.12974

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  • Multi-modal Cycle-Consistent Generalized Zero-Shot Learning

    Felix R., Vijay Kumar B.G., Reid I., Carneiro G. (2018) Multi-modal Cycle-Consistent Generalized Zero-Shot Learning. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11210. Springer, Cham. https://doi.org/10.1007/978-3-030-01231-1_2

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  • AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

    Do, T.-T., Nguyen, A., & Reid, I. (2018). AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–5). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8460902

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  • Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

    Han, X., Lu, J., Zhao, C., You, S., & Li, H. (2018). Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters, 25(4), 551–555. http://doi.org/10.1109/LSP.2018.2809685

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  • Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

    Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H.-Y. M. (2018). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. IEEE Transactions on Multimedia, 20(8), 2073–2085. http://doi.org/10.1109/TMM.2018.2794262

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  • Visual Grounding via Accumulated Attention

    Deng, C., Wu, Q., Wu, Q., Hu, F., Lyu, F., & Tan, M. (2018). Visual Grounding via Accumulated Attention. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7746–7755). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00808

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  • Vision Based Forward Sensitive Reactive Control for a Quadrotor VTOL

    Stevens, J.-L., & Mahony, R. (2018). Vision Based Forward Sensitive Reactive Control for a Quadrotor VTOL. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5232–5238). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593606

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  • Calibrating Light-Field Cameras Using Plenoptic Disc Features

    O’brien, S., Trumpf, J., Ila, V., & Mahony, R. (2018). Calibrating Light-Field Cameras Using Plenoptic Disc Features. In 2018 International Conference on 3D Vision (3DV) (pp. 286–294). Verona, Italy: IEEE. http://doi.org/10.1109/3DV.2018.00041

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  • A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras

    O’Brien, S. G. P., Trumpf, J., Ila, V., & Mahony, R. (2018). A Geometric Observer for Scene Reconstruction Using Plenoptic Cameras. In 2018 IEEE Conference on Decision and Control (CDC) (pp. 557–564). Florida, United States: IEEE. http://doi.org/10.1109/CDC.2018.8618954

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  • Homography estimation of a moving planar scene from direct point correspondence

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

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  • Video Representation Learning Using Discriminative Pooling

    Wang, J., Cherian, A., Porikli, F., & Gould, S. (2018). Video Representation Learning Using Discriminative Pooling. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1149–1158). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00126

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  • Non-linear Temporal Subspace Representations for Activity Recognition

    Cherian, A., Sra, S., Gould, S., & Hartley, R. (2018). Non-linear Temporal Subspace Representations for Activity Recognition. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2197–2206). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00234

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  • One-class Gaussian process regressor for quality assessment of transperineal ultrasound images

    Camps, S. M., Houben, T., Fontanarosa, D., Edwards, C., Antico, M., Dunnhofer, M., Martens, E.G.H.J, Baeza, J.A., Vanneste, B.G.L., van Limbergen, E.J., de W., Peter, H.N., Verhaegen, F., & Carneiro, G. (2018). One-class Gaussian process regressor for quality assessment of transperineal ultrasound images. In International Conference on Medical Imaging with Deep Learning (MIDL). Amsterdam. Retrieved from https://eprints.qut.edu.au/120113/

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  • Action Recognition with Dynamic Image Networks

    Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2018). Action Recognition with Dynamic Image Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2799–2813. http://doi.org/10.1109/TPAMI.2017.2769085

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  • Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments

    Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sunderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018). Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3674–3683). IEEE. http://doi.org/10.1109/CVPR.2018.00387

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  • Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

    Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6077–6086). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00636

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  • Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

    Wang, H., Xiao, G., Yan, Y., & Suter, D. (2019). Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), 697–711. https://doi.org/10.1109/TPAMI.2018.2803173

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  • Semantics-Aware Visual Object Tracking

    Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2019). Semantics-aware visual object tracking. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1687–1700. https://doi.org/10.1109/TCSVT.2018.2848358

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  • Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

    Chen, Z., Liu, L., Sa, I., Ge, Z., & Chli, M. (2018). Learning Context Flexible Attention Model for Long-Term Visual Place Recognition. IEEE Robotics and Automation Letters, 3(4), 4015–4022. http://doi.org/10.1109/LRA.2018.2859916

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  • Unsupervised Domain Adaptation Using Robust Class-Wise Matching

    Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., Van Den Hengel, A., & Zhang, Y. (2019). Unsupervised Domain Adaptation Using Robust Class-Wise Matching. IEEE Transactions on Circuits and Systems for Video Technology, 29(5), 1339–1349. https://doi.org/10.1109/TCSVT.2018.2842206

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  • Practical Motion Segmentation for Urban Street View Scenes

    Rubino, C., Del Bue, A., & Chin, T.-J. (2018). Practical Motion Segmentation for Urban Street View Scenes. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1879–1886). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8460993

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  • VITAL: VIsual Tracking via Adversarial Learning

    Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, Rynson W.H., & Yang, M.-H. (2018). VITAL: VIsual Tracking via Adversarial Learning. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8990–8999). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00937

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  • A Fast Resection-Intersection Method for the Known Rotation Problem

    Zhang, Q., Chin, T.-J., & Le, H. M. (2018). A Fast Resection-Intersection Method for the Known Rotation Problem. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 3012–3021). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00318

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  • Rotation Averaging and Strong Duality

    Eriksson, A., Olsson, C., Kahl, F., & Chin, T.-J. (2018). Rotation Averaging and Strong Duality. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 127–135). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00021

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  • ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery

    Marmol, A., Corke, P., & Peynot, T. (2018). ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3882–3889). Madrid, Spain: IEEE. https://doi.org/10.1109/IROS.2018.8593501

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  • QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

    Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. https://doi.org/10.1109/LRA.2018.2866205

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  • Collaborative Planning for Mixed-Autonomy Lane Merging

    Bansal, S., Cosgun, A., Nakhaei, A., & Fujimura, K. (2018). Collaborative Planning for Mixed-Autonomy Lane Merging. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4449–4455). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594197

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  • Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

    Wang, X., Şekercioğlu, Y., Drummond, T., Frémont, V., Natalizio, E., & Fantoni, I. (2018). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Sensors, 18(8), 2430. http://doi.org/10.3390/s18082430

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  • CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

    Spek, A., Dharmasiri, T., & Drummond, T. (2018). CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 540–547). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594243

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  • Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

    Meyer, B. J., Harwood, B., & Drummond, T. (2018). Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels. In IEEE International Conference on Image Processing (ICIP) (pp. 151–155). Athens, Greece: IEEE. http://doi.org/10.1109/ICIP.2018.8451297

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  • Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

    Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2876413

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  • A review of deep learning in the study of materials degradation

    Nash, W., Drummond, T., & Birbilis, N. (2018). A review of deep learning in the study of materials degradation. Npj Materials Degradation, 2(1), 37. http://doi.org/10.1038/s41529-018-0058-x

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  • An Extended Filtered Channel Framework for Pedestrian Detection

    You, M., Zhang, Y., Shen, C., & Zhang, X. (2018). An Extended Filtered Channel Framework for Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1640–1651. https://doi.org/10.1109/TITS.2018.2807199

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  • An Embarrassingly Simple Approach to Visual Domain Adaptation

    Lu, H., Shen, C., Cao, Z., Xiao, Y., & van den Hengel, A. (2018). An Embarrassingly Simple Approach to Visual Domain Adaptation. IEEE Transactions on Image Processing, 27(7), 3403–3417. https://doi.org/10.1109/TIP.2018.2819503

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  • Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction

    Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction. International Journal of Computer Vision, 126(8), 797–821. https://doi.org/10.1007/s11263-018-1080-8

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  • Multi-label learning based deep transfer neural network for facial attribute classification

    Zhuang, N., Yan, Y., Chen, S., Wang, H., & Shen, C. (2018). Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recognition, 80, 225–240. https://doi.org/10.1016/J.PATCOG.2018.03.018

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  • Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking

    Li, X., Zhao, L., Ji, W., Wu, Y., Wu, F., Yang, M.-H., Dacheng, T., Reid, I. (2018). Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2018.2818132 *In Press

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  • The limits and potentials of deep learning for robotics

    Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., & Corke, P. (2018). The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4–5), 405–420. http://doi.org/10.1177/0278364918770733

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  • Automating analysis of vegetation with computer vision: Cover estimates and classification

    McCool, C., Beattie, J., Milford, M., Bakker, J. D., Moore, J. L., & Firn, J. (2018). Automating analysis of vegetation with computer vision: Cover estimates and classification. Ecology and Evolution, 8(12), 6005–6015. http://doi.org/10.1002/ece3.4135

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  • A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration

    Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11320 LNAI, 759–765. https://doi.org/10.1007/978-3-030-03991-2_68

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  • A rapidly deployable classification system using visual data for the application of precision weed management

    Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107–120. http://doi.org/10.1016/J.COMPAG.2018.02.023

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  • SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

    Pham, T. T., Do, T.-T., Sunderhauf, N., & Reid, I. (2018). SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–9). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8461108

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  • Measures of incentives and confidence in using a social robot

    Robinson, N. L., Connolly, J., Johnson, G. M., Kim, Y., Hides, L., & Kavanagh, D. J. (2018). Measures of incentives and confidence in using a social robot. Science Robotics, 3(21), eaat6963. http://doi.org/10.1126/scirobotics.aat6963

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  • Glare-free retinal imaging using a portable light field fundus camera

    Palmer, D. W., Coppin, T., Rana, K., Dansereau, D. G., Suheimat, M., Maynard, M. Atchison, D. A., Roberts, J., Crawford, R., & Jaiprakash, A. (2018). Glare-free retinal imaging using a portable light field fundus camera. Biomedical Optics Express, 9(7), 3178. http://doi.org/10.1364/BOE.9.003178

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  • Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

    James, J., Ford, J. J., & Molloy, T. L. (2018). Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems. IEEE Robotics and Automation Letters, 3(4), 4383–4390. http://doi.org/10.1109/LRA.2018.2867237

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  • Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

    Jacobson, A., Chen, Z., & Milford, M. (2018). Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, 1–17. http://doi.org/10.1007/s00422-017-0745-7

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  • Bootstrapping the Performance of Webly Supervised Semantic Segmentation

    Shen, T., Lin, G., Shen, C., & Reid, I. (2018). Bootstrapping the Performance of Webly Supervised Semantic Segmentation. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 1363–1371). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00148

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  • Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

    Kumar, S., Cherian, A., Dai, Y., & Li, H. (2018). Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 254–263). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00034

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  • Output regulation for systems on matrix Lie-groups

    de Marco, S., Marconi, L., Mahony, R., & Hamel, T. (2018). Output regulation for systems on matrix Lie-groups. Automatica, 87, 8–16. https://doi.org/10.1016/J.AUTOMATICA.2017.08.006

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  • Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost.

    Yu, L., Jacobson, A., & Milford, M. (2018). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost. IEEE Robotics and Automation Letters, 3(2), 811–818. http://doi.org/10.1109/LRA.2018.2792144

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  • Multimodal Trip Hazard Affordance Detection on Construction Sites

    McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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  • Special issue on deep learning in robotics

    Sünderhauf, N., Leitner, J., Upcroft, B., & Roy, N. (2018, April 27). Special issue on deep learning in robotics. The International Journal of Robotics Research. SAGE PublicationsSage UK: London, England. http://doi.org/10.1177/0278364918769189

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  • Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

    *Weerasekera, C. S., Dharmasiri, T., Garg, R., Drummond, T., & Reid, I. (2018). Just-in-Time Reconstruction: Inpainting Sparse Maps Using Single View Depth Predictors as Priors. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–9). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8460549

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  • Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

    Park, C., Moghadam, P., Kim, S., Elfes, A., Fookes, C., & Sridharan, S. (2017). Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM. Retrieved from http://arxiv.org/abs/1711.01691

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  • Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

    Morrison, D., Tow, A. W., McTaggart, M., Smith, R., Kelly-Boxall, N., Wade-McCue, S., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Lee, D., Milan, A., Pham, T., Rallos, G., Razjigaev, A., Rowntree, T., Kumar, V., Zhuang, Z., Lehnert, C., Reid, I., Corke, P., and Leitner, J. (2018). Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7757–7764). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8463191

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  • Dropout Sampling for Robust Object Detection in Open-Set Conditions

    Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. Proceedings - IEEE International Conference on Robotics and Automation, 3243–3249. https://doi.org/10.1109/ICRA.2018.8460700

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  • Semantic Segmentation from Limited Training Data

    Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, K., Lee, D., McTaggart, M., Rallos, G., Razjigaev, A., Rowntree, T., Shen, T., Smith, R., Wade-McCue, S., Zhuang, Z., Lehnert, C., Lin, G., Reid, I., Corke, P., and Leitner, J. (2018). Semantic Segmentation from Limited Training Data. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1908–1915). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8461082

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  • Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

    McCool, C. S., Beattie, J., Firn, J., Lehnert, C., Kulk, J., Bawden, O., Russell, R., & Perez, T. (2018). Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 1–1. http://doi.org/10.1109/LRA.2018.2794619

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  • Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

    Latif, Y., Garg, R., Milford, M., & Reid, I. (2018). Addressing challenging place recognition tasks using generative adversarial networks. Proceedings - IEEE International Conference on Robotics and Automation, 2349–2355. https://doi.org/10.1109/ICRA.2018.8461081

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  • Multi-Modal Trip Hazard Affordance Detection On Construction Sites

    McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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  • Image Captioning and Visual Question Answering Based on Attributes and External Knowledge

    Wu, Q., Shen, C., Wang, P., Dick, A., & Van Den Hengel, A. (2018). Image Captioning and Visual Question Answering Based on Attributes and External Knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1367–1381. https://doi.org/10.1109/TPAMI.2017.2708709

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Journal Articles

  • Monocular Depth Estimation with Augmented Ordinal Depth Relationships

    Cao, Y., Zhao, T., Xian, K., Shen, C., Cao, Z., & Xu, S. (2018). Monocular Depth Estimation with Augmented Ordinal Depth Relationships. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2018.2877944

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  • An adaptive localization system for image storage and localization latency requirements

    Mao, J., Hu, X., & Milford, M. (2018). An adaptive localization system for image storage and localization latency requirements. Robotics and Autonomous Systems, 107, 246–261. http://doi.org/10.1016/J.ROBOT.2018.06.007

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  • Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection

    James, J., Ford, J. J., & Molloy, T. L. (2018). Quickest Detection of Intermittent Signals With Application to Vision-Based Aircraft Detection. IEEE Transactions on Control Systems Technology, 1–8. http://doi.org/10.1109/TCST.2018.2872468

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  • Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation

    Campbell, D. J., Petersson, L., Kneip, L., & Li, H. (2018). Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2018.2848650

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  • Dense 3D Face Correspondence

    Gilani, S. Z., Mian, A., Shafait, F., & Reid, I. (2018). Dense 3D Face Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7), 1584–1598. https://doi.org/10.1109/TPAMI.2017.2725279

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  • Coresets for Triangulation

    Gilani, S. Z., Mian, A., Shafait, F., & Reid, I. (2018). Dense 3D Face Correspondence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(7), 1584–1598. https://doi.org/10.1109/TPAMI.2017.2725279

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  • The Role of Symmetry in Rigidity Analysis: A Tool for Network Localisation and Formation Control

    Stacey, G., & Mahony, R. (2018). The Role of Symmetry in Rigidity Analysis: A Tool for Network Localization and Formation Control. IEEE Transactions on Automatic Control, 63(5), 1313–1328. https://doi.org/10.1109/TAC.2017.2747760

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  • Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

    Saleh, F. S., Aliakbarian, M. S., Salzmann, M., Petersson, L., Alvarez, J. M., & Gould, S. (2018). Incorporating Network Built-in Priors in Weakly-Supervised Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1382–1396. https://doi.org/10.1109/TPAMI.2017.2713785

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  • Guaranteed Outlier Removal for Point Cloud Registration with Correspondences

    Guaranteed Outlier Removal for Point Cloud Registration with Correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2868–2882. https://doi.org/10.1109/TPAMI.2017.2773482

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  • Structured Learning of Tree Potentials in CRF for Image Segmentation

    Liu, F., Lin, G., Qiao, R., & Shen, C. (2017). Structured Learning of Tree Potentials in CRF for Image Segmentation. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1–7. http://doi.org/10.1109/TNNLS.2017.2690453 *In Press

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  • Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods

    Harandi, M., Salzmann, M., & Hartley, R. (2018). Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(1), 48–62. https://doi.org/10.1109/TPAMI.2017.2655048

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  • Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

    Zhu, G., Porikli, F., & Li, H. (2018). Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, 28(2), 314–326. http://doi.org/10.1109/TCSVT.2016.2615518

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  • Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine

    Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2018). Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine. Journal of Digital Imaging, 31(6), 869–878. http://doi.org/10.1007/s10278-018-0084-9

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  • Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing

    Liu, W., Zhang, P., Chen, X., Shen, C., Huang, X., & Yang, J. (2018). Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing. IEEE Transactions on Circuits and Systems for Video Technology, 1–1. http://doi.org/10.1109/TCSVT.2018.2890202

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  • Robust and Efficient Relative Pose With a Multi-Camera System for Autonomous Driving in Highly Dynamic Environments

    Liu, L., Li, H., Dai, Y., & Pan, Q. (2018). Robust and efficient relative pose with a Multi-Camera system for autonomous driving in highly dynamic environments. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2432–2444. https://doi.org/10.1109/TITS.2017.2749409

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  • Reading car license plates using deep neural networks

    Li, H., Wang, P., You, M., & Shen, C. (2018). Reading car license plates using deep neural networks. Image and Vision Computing, 72, 14–23. http://doi.org/10.1016/J.IMAVIS.2018.02.002

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  • Exploring Context with Deep Structured Models for Semantic Segmentation

    Lin, G., Shen, C., van den Hengel, A., & Reid, I. (2018). Exploring Context with Deep Structured Models for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1352–1366. http://doi.org/10.1109/TPAMI.2017.2708714

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  • Drones count wildlife more accurately and precisely than humans

    Hodgson, J. C., Mott, R., Baylis, S. M., Pham, T. T., Wotherspoon, S., Kilpatrick, A. D., Ramesh, R.S., Reid, I., Terauds, A., & Koh, L. P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160–1167. http://doi.org/10.1111/2041-210X.12974

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  • Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

    Han, X., Lu, J., Zhao, C., You, S., & Li, H. (2018). Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks. IEEE Signal Processing Letters, 25(4), 551–555. http://doi.org/10.1109/LSP.2018.2809685

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  • Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression

    Guo, G., Wang, H., Shen, C., Yan, Y., & Liao, H.-Y. M. (2018). Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression. IEEE Transactions on Multimedia, 20(8), 2073–2085. http://doi.org/10.1109/TMM.2018.2794262

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  • Action Recognition with Dynamic Image Networks

    Bilen, H., Fernando, B., Gavves, E., & Vedaldi, A. (2018). Action Recognition with Dynamic Image Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(12), 2799–2813. http://doi.org/10.1109/TPAMI.2017.2769085

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  • Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

    Wang, H., Xiao, G., Yan, Y., & Suter, D. (2019). Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(3), 697–711. https://doi.org/10.1109/TPAMI.2018.2803173

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  • Semantics-Aware Visual Object Tracking

    Yao, R., Lin, G., Shen, C., Zhang, Y., & Shi, Q. (2019). Semantics-aware visual object tracking. IEEE Transactions on Circuits and Systems for Video Technology, 29(6), 1687–1700. https://doi.org/10.1109/TCSVT.2018.2848358

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  • Learning Context Flexible Attention Model for Long-Term Visual Place Recognition

    Chen, Z., Liu, L., Sa, I., Ge, Z., & Chli, M. (2018). Learning Context Flexible Attention Model for Long-Term Visual Place Recognition. IEEE Robotics and Automation Letters, 3(4), 4015–4022. http://doi.org/10.1109/LRA.2018.2859916

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  • Unsupervised Domain Adaptation Using Robust Class-Wise Matching

    Zhang, L., Wang, P., Wei, W., Lu, H., Shen, C., Van Den Hengel, A., & Zhang, Y. (2019). Unsupervised Domain Adaptation Using Robust Class-Wise Matching. IEEE Transactions on Circuits and Systems for Video Technology, 29(5), 1339–1349. https://doi.org/10.1109/TCSVT.2018.2842206

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  • QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

    Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. https://doi.org/10.1109/LRA.2018.2866205

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  • Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks

    Wang, X., Şekercioğlu, Y., Drummond, T., Frémont, V., Natalizio, E., & Fantoni, I. (2018). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Sensors, 18(8), 2430. http://doi.org/10.3390/s18082430

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  • Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

    Liao, Z., Drummond, T., Reid, I., & Carneiro, G. (2018). Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. http://doi.org/10.1109/TPAMI.2018.2876413

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  • A review of deep learning in the study of materials degradation

    Nash, W., Drummond, T., & Birbilis, N. (2018). A review of deep learning in the study of materials degradation. Npj Materials Degradation, 2(1), 37. http://doi.org/10.1038/s41529-018-0058-x

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  • An Extended Filtered Channel Framework for Pedestrian Detection

    You, M., Zhang, Y., Shen, C., & Zhang, X. (2018). An Extended Filtered Channel Framework for Pedestrian Detection. IEEE Transactions on Intelligent Transportation Systems, 19(5), 1640–1651. https://doi.org/10.1109/TITS.2018.2807199

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  • An Embarrassingly Simple Approach to Visual Domain Adaptation

    Lu, H., Shen, C., Cao, Z., Xiao, Y., & van den Hengel, A. (2018). An Embarrassingly Simple Approach to Visual Domain Adaptation. IEEE Transactions on Image Processing, 27(7), 3403–3417. https://doi.org/10.1109/TIP.2018.2819503

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  • Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction

    Zhang, L., Wei, W., Zhang, Y., Shen, C., van den Hengel, A., & Shi, Q. (2018). Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction. International Journal of Computer Vision, 126(8), 797–821. https://doi.org/10.1007/s11263-018-1080-8

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  • Multi-label learning based deep transfer neural network for facial attribute classification

    Zhuang, N., Yan, Y., Chen, S., Wang, H., & Shen, C. (2018). Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recognition, 80, 225–240. https://doi.org/10.1016/J.PATCOG.2018.03.018

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  • Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking

    Li, X., Zhao, L., Ji, W., Wu, Y., Wu, F., Yang, M.-H., Dacheng, T., Reid, I. (2018). Multi-Task Structure-aware Context Modeling for Robust Keypoint-based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/TPAMI.2018.2818132 *In Press

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  • The limits and potentials of deep learning for robotics

    Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., & Corke, P. (2018). The limits and potentials of deep learning for robotics. The International Journal of Robotics Research, 37(4–5), 405–420. http://doi.org/10.1177/0278364918770733

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  • Automating analysis of vegetation with computer vision: Cover estimates and classification

    McCool, C., Beattie, J., Milford, M., Bakker, J. D., Moore, J. L., & Firn, J. (2018). Automating analysis of vegetation with computer vision: Cover estimates and classification. Ecology and Evolution, 8(12), 6005–6015. http://doi.org/10.1002/ece3.4135

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  • A rapidly deployable classification system using visual data for the application of precision weed management

    Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107–120. http://doi.org/10.1016/J.COMPAG.2018.02.023

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  • Measures of incentives and confidence in using a social robot

    Robinson, N. L., Connolly, J., Johnson, G. M., Kim, Y., Hides, L., & Kavanagh, D. J. (2018). Measures of incentives and confidence in using a social robot. Science Robotics, 3(21), eaat6963. http://doi.org/10.1126/scirobotics.aat6963

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  • Glare-free retinal imaging using a portable light field fundus camera

    Palmer, D. W., Coppin, T., Rana, K., Dansereau, D. G., Suheimat, M., Maynard, M. Atchison, D. A., Roberts, J., Crawford, R., & Jaiprakash, A. (2018). Glare-free retinal imaging using a portable light field fundus camera. Biomedical Optics Express, 9(7), 3178. http://doi.org/10.1364/BOE.9.003178

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  • Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

    James, J., Ford, J. J., & Molloy, T. L. (2018). Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems. IEEE Robotics and Automation Letters, 3(4), 4383–4390. http://doi.org/10.1109/LRA.2018.2867237

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  • Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework

    Jacobson, A., Chen, Z., & Milford, M. (2018). Leveraging variable sensor spatial acuity with a homogeneous, multi-scale place recognition framework. Biological Cybernetics, 1–17. http://doi.org/10.1007/s00422-017-0745-7

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  • Output regulation for systems on matrix Lie-groups

    de Marco, S., Marconi, L., Mahony, R., & Hamel, T. (2018). Output regulation for systems on matrix Lie-groups. Automatica, 87, 8–16. https://doi.org/10.1016/J.AUTOMATICA.2017.08.006

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  • Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost.

    Yu, L., Jacobson, A., & Milford, M. (2018). Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sublinear Storage Cost. IEEE Robotics and Automation Letters, 3(2), 811–818. http://doi.org/10.1109/LRA.2018.2792144

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  • Multimodal Trip Hazard Affordance Detection on Construction Sites

    McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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  • Special issue on deep learning in robotics

    Sünderhauf, N., Leitner, J., Upcroft, B., & Roy, N. (2018, April 27). Special issue on deep learning in robotics. The International Journal of Robotics Research. SAGE PublicationsSage UK: London, England. http://doi.org/10.1177/0278364918769189

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  • Multi-Modal Trip Hazard Affordance Detection On Construction Sites

    McMahon, S., Sunderhauf, N., Upcroft, B., & Milford, M. (2018). Multimodal Trip Hazard Affordance Detection on Construction Sites. IEEE Robotics and Automation Letters, 3(1), 1–8. http://doi.org/10.1109/LRA.2017.2719763

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  • Image Captioning and Visual Question Answering Based on Attributes and External Knowledge

    Wu, Q., Shen, C., Wang, P., Dick, A., & Van Den Hengel, A. (2018). Image Captioning and Visual Question Answering Based on Attributes and External Knowledge. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(6), 1367–1381. https://doi.org/10.1109/TPAMI.2017.2708709

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Conference Papers

  • Learning to Predict Crisp Boundaries

    Deng R., Shen C., Liu S., Wang H., Liu X. (2018) Learning to Predict Crisp Boundaries. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11210. Springer, Cham. https://doi.org/10.1007/978-3-030-01231-1_35

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  • Robust Fitting in Computer Vision: Easy or Hard?

    Chin TJ., Cai Z., Neumann F. (2018) Robust Fitting in Computer Vision: Easy or Hard?. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham. https://doi.org/10.1007/978-3-030-01258-8_43

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  • Deterministic Consensus Maximization with Biconvex Programming

    Cai Z., Chin TJ., Le H., Suter D. (2018) Deterministic Consensus Maximization with Biconvex Programming. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11216. Springer, Cham. https://doi.org/10.1007/978-3-030-01258-8_42

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  • A Binary Optimization Approach for Constrained K-Means Clustering

    Le H.M., Eriksson A., Do TT., Milford M. (2019) A Binary Optimization Approach for Constrained K-Means Clustering. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_24

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  • Traversing Latent Space using Decision Ferns

    Zuo Y., Avraham G., Drummond T. (2019) Traversing Latent Space Using Decision Ferns. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_37

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  • Stereo Computation for a Single Mixture Image

    Zhong Y., Dai Y., Li H. (2018) Stereo Computation for a Single Mixture Image. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11213. Springer, Cham. https://doi.org/10.1007/978-3-030-01240-3_2

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  • Learning Free-Form Deformations for 3D Object Reconstruction

    Jack, D., Pontes, J. K., Sridharan, S., Fookes, C., Shirazi, S., Maire, F., & Eriksson, A. (2019). Learning Free-Form Deformations for 3D Object Reconstruction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11362 LNCS, 317–333. https://doi.org/10.1007/978-3-030-20890-5_21

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  • Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

    Zhan, H., Garg, R., Weerasekera, C. S., Li, K., Agarwal, H., & Reid, I. M. (2018). Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 340–349). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00043

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  • Discrimination-aware channel pruning for deep neural networks

    Zhuang, Z., Tan, M., Zhuang, B., Liu, J., Guo, Y., Wu, Q., Huang, J., & Zhu, J. (2018). Discrimination-aware Channel Pruning for Deep Neural Networks. Advances in Neural Information Processing Systems, 2018-December, 875–886.

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  • OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions

    Talbot, B., Garg, S., & Milford, M. (2018). OpenSeqSLAM2.0: An Open Source Toolbox for Visual Place Recognition Under Changing Conditions. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7758–7765). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593761

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  • Scalable Deep k-Subspace Clustering

    Zhang T., Ji P., Harandi M., Hartley R., Reid I. (2019) Scalable Deep k-Subspace Clustering. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_30

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  • Continuous-Time Intensity Estimation Using Event Cameras

    Scheerlinck C., Barnes N., Mahony R. (2019) Continuous-Time Intensity Estimation Using Event Cameras. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_20

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  • Action Anticipation by Predicting Future Dynamic Images

    Rodriguez C., Fernando B., Li H. (2019) Action Anticipation by Predicting Future Dynamic Images. In: Leal-Taixé L., Roth S. (eds) Computer Vision – ECCV 2018 Workshops. ECCV 2018. Lecture Notes in Computer Science, vol 11131. Springer, Cham. https://doi.org/10.1007/978-3-030-11015-4_10

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  • Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles

    *McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5719–5726). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8593529

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  • Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

    Morrison, D., Corke, P., & Leitner, J. (2018). Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach. Retrieved from http://arxiv.org/abs/1804.05172

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  • Efficient Subpixel Refinement with Symbolic Linear Predictors

    Lui, V., Geeves, J., Yii, W., & Drummond, T. (2018). Efficient Subpixel Refinement with Symbolic Linear Predictors. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8165–8173). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00852

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  • Structure Aware SLAM Using Quadrics and Planes

    Hosseinzadeh M., Latif Y., Pham T., Suenderhauf N., Reid I. (2019) Structure Aware SLAM Using Quadrics and Planes. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11363. Springer, Cham. https://doi.org/10.1007/978-3-030-20893-6_26

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  • Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

    Hausler, S., Jacobson, A., & Milford, M. (2018). Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration. Retrieved from http://arxiv.org/abs/1810.12465

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  • LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

    Garg, S., Suenderhauf, N., & Milford, M. (2018). LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics. Retrieved from http://arxiv.org/abs/1804.05526

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  • An End-to-End TextSpotter with Explicit Alignment and Attention

    He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., & Sun, C. (2018). An End-to-End TextSpotter with Explicit Alignment and Attention. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5020–5029). Salt Lake City, United States: IEEE. http://doi.org/10.1109/CVPR.2018.00527

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  • Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

    Garg, S., Suenderhauf, N., & Milford, M. (2018). Don’t Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 3645–3652). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8461051

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  • ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs

    Dharmasiri T., Spek A., Drummond T. (2019) ENG: End-to-End Neural Geometry for Robust Depth and Pose Estimation Using CNNs. In: Jawahar C., Li H., Mori G., Schindler K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_39

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  • Neural Algebra of Classifiers

    *Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2018). Neural Algebra of Classifiers. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 729–737). IEEE. https://doi.org/10.1109/WACV.2018.00085

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  • Towards vision-based manipulation of plastic materials

    *Cherubini, A., Leitner, J., Ortenzi, V., & Corke, P. (2018). Towards vision-based manipulation of plastic materials. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 485–490). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594108

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  • Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal

    Bruce, J., Sünderhauf, N., Mirowski, P., Hadsell, R., & Milford, M. (2018). Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal. Retrieved from http://arxiv.org/abs/1807.05211

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  • Training Deep Neural Networks for Visual Servoing

    *Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., & Corke, P. (2018). Training Deep Neural Networks for Visual Servoing. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1–8). Brisbane, Australia: IEEE. http://doi.org/10.1109/ICRA.2018.8461068

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  • VIENA 2: A Driving Anticipation Dataset

    Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2019). VIENA2: A Driving Anticipation Dataset. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11361 LNCS, 449–466. https://doi.org/10.1007/978-3-030-20887-5_28

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  • Model-free and learning-free grasping by Local Contact Moment matching

    *Adjigble, M., Marturi, N., Ortenzi, V., Rajasekaran, V., Corke, P., & Stolkin, R. (2018). Model-free and learning-free grasping by Local Contact Moment matching. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2933–2940). Madrid, Spain: IEEE. http://doi.org/10.1109/IROS.2018.8594226

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  • Deja vu: Scalable Place Recognition Using Mutually Supportive Feature Frequencies

    Jacobson, A., Scheirer, W., & Milford, M. (2017). Déjà vu: Scalable place recognition using mutually supportive feature frequencies. IEEE International Conference on Intelligent Robots and Systems, 2017-September, 6654–6661. https://doi.org/10.1109/IROS.2017.8206580

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  • Design of a multi-modal end-effector and grasping system- How integrated design helped win the Amazon Robotics Challenge

    Kelly-Boxall, N., Morrison, D., Wade-McCue, S., Corke, P., & Leitner, J. (2018). Design of a multi-modal end-effector and grasping system- How integrated design helped win the amazon robotics challenge. Australasian Conference on Robotics and Automation, ACRA, 2018-December.

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  • On the structure of kinematic systems with complete symmetry

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

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  • Seeing Deeply and Bidirectionally: A Deep Learning Approach for Single Image Reflection Removal

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  • Deblurring Natural Image Using Super-Gaussian Fields

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  • Adversarial Training of Variational Auto-Encoders for High Fidelity Image Generation

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  • Semi-dense 3D Reconstruction with a Stereo Event Camera

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  • 3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

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  • Open-World Stereo Video Matching with Deep RNN

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  • Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective

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  • Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables

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  • Robust Visual Odometry in Underwater Environment

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  • Goal-Oriented Visual Question Generation via Intermediate Rewards

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

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  • Face Super-Resolution Guided by Facial Component Heatmaps

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  • Learning Discriminative Video Representations Using Adversarial Perturbations

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  • Structure from Recurrent Motion: From Rigidity to Recurrency

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  • Kernel Support Vector Machines and Convolutional Neural Networks

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  • Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking

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  • Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries

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  • Towards Effective Low-Bitwidth Convolutional Neural Networks

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  • Are You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning

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  • Bayesian Semantic Instance Segmentation in Open Set World

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  • Training Medical Image Analysis Systems like Radiologists

    Maicas G., Bradley A.P., Nascimento J.C., Reid I., Carneiro G. (2018) Training Medical Image Analysis Systems like Radiologists. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham. https://doi.org/10.1007/978-3-030-00928-1_62

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  • Visual Question Answering with Memory-Augmented Networks

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  • Deep Regression Tracking with Shrinkage Loss

    Lu X., Ma C., Ni B., Yang X., Reid I., Yang MH. (2018) Deep Regression Tracking with Shrinkage Loss. In: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol 11218. Springer, Cham. https://doi.org/10.1007/978-3-030-01264-9_22

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  • Efficient Dense Point Cloud Object Reconstruction Using Deformation Vector Fields

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  • Multi-modal Cycle-Consistent Generalized Zero-Shot Learning

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  • AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection

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  • Visual Grounding via Accumulated Attention

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  • Vision Based Forward Sensitive Reactive Control for a Quadrotor VTOL

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  • Calibrating Light-Field Cameras Using Plenoptic Disc Features

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  • Homography estimation of a moving planar scene from direct point correspondence

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  • Video Representation Learning Using Discriminative Pooling

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  • Non-linear Temporal Subspace Representations for Activity Recognition

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  • One-class Gaussian process regressor for quality assessment of transperineal ultrasound images

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  • Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

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  • A Fast Resection-Intersection Method for the Known Rotation Problem

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  • Rotation Averaging and Strong Duality

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  • ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery

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  • Collaborative Planning for Mixed-Autonomy Lane Merging

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  • CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks

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  • Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

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  • A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration

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  • SceneCut: Joint Geometric and Object Segmentation for Indoor Scenes

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  • Bootstrapping the Performance of Webly Supervised Semantic Segmentation

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  • Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective

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  • Just-In-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors

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  • Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM

    Park, C., Moghadam, P., Kim, S., Elfes, A., Fookes, C., & Sridharan, S. (2017). Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM. Retrieved from http://arxiv.org/abs/1711.01691

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  • Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge

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  • Dropout Sampling for Robust Object Detection in Open-Set Conditions

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  • Semantic Segmentation from Limited Training Data

    Milan, A., Pham, T., Vijay, K., Morrison, D., Tow, A. W., Liu, L., Erskine, J., Grinover, R., Gurman, A., Hunn, T., Kelly-Boxall, K., Lee, D., McTaggart, M., Rallos, G., Razjigaev, A., Rowntree, T., Shen, T., Smith, R., Wade-McCue, S., Zhuang, Z., Lehnert, C., Lin, G., Reid, I., Corke, P., and Leitner, J. (2018). Semantic Segmentation from Limited Training Data. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1908–1915). Brisbane: IEEE. http://doi.org/10.1109/ICRA.2018.8461082

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  • Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

    McCool, C. S., Beattie, J., Firn, J., Lehnert, C., Kulk, J., Bawden, O., Russell, R., & Perez, T. (2018). Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics. IEEE Robotics and Automation Letters, 1–1. http://doi.org/10.1109/LRA.2018.2794619

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  • Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks

    Latif, Y., Garg, R., Milford, M., & Reid, I. (2018). Addressing challenging place recognition tasks using generative adversarial networks. Proceedings - IEEE International Conference on Robotics and Automation, 2349–2355. https://doi.org/10.1109/ICRA.2018.8461081

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Edited Collection

  • Special issue on deep learning in robotics

    Sünderhauf, N., Leitner, J., Upcroft, B., & Roy, N. (2018, April 27). Special issue on deep learning in robotics. The International Journal of Robotics Research. SAGE PublicationsSage UK: London, England. http://doi.org/10.1177/0278364918769189

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