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Publications

2017 Submitted [44]

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. (2017). Semantic Segmentation from Limited Training Data. Retrieved from https://arxiv.org/abs/1709.07665

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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. (2017). Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge. Retrieved from https://arxiv.org/abs/1709.06283

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Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks

Zhang, F., Leitner, J., Milford, M., & Corke, P. (2017). Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks. Retrieved from https://arxiv.org/abs/1709.05746

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Towards Unsupervised Weed Scouting for Agricultural Robotics

Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards Unsupervised Weed Scouting for Agricultural Robotics. Retrieved from http://arxiv.org/abs/1702.01247

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

Wang, X., Sekercioglu, Y.A., Drummond, T., Fremont, V., Natalizio, E., Fantoni, I. (2017). Relative Pose Based Redundancy Removal: Collaborative RGB-D Data Transmission in Mobile Visual Sensor Networks. Retrieved from arXiv preprint arXiv:1707.05978

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A Fast Method for Computing Principal Curvatures from Range Images.

Spek, A., Li, W.H., Drummond, T., (2017). A Fast Method for Computing Principal Curvatures from Range Images. Retrieved from arXiv preprint arXiv:1707.00385

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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. (2017). Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation. Retrieved from https://arxiv.org/pdf/1706.02189.pdf

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Second-order Temporal Pooling for Action Recognition

Cherian, A., & Gould, S. (2017). Second-order Temporal Pooling for Action Recognition. Retrieved from http://arxiv.org/abs/1704.06925

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DeepPermNet: Visual Permutation Learning

Cruz, R. S., Fernando, B., Cherian, A., & Gould, S. (2017). DeepPermNet: Visual Permutation Learning. Retrieved from https://arxiv.org/abs/1704.02729

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Generalized Rank Pooling for Activity Recognition

Cherian, A., Fernando, B., Harandi, M., & Gould, S. (2017). Generalized Rank Pooling for Activity Recognition. Retrieved from https://arxiv.org/abs/1704.02112

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Action Representation Using Classifier Decision Boundaries

Wang, J., Cherian, A., Porikli, F., & Gould, S. (2017). Action Representation Using Classifier Decision Boundaries. Retrieved from http://arxiv.org/abs/1704.01716

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Encouraging LSTMs to Anticipate Actions Very Early

Aliakbarian, M. S., Saleh, F., Salzmann, M., Fernando, B., Petersson, L., & Andersson, L. (2017). Encouraging LSTMs to Anticipate Actions Very Early. Retrieved from http://arxiv.org/abs/1703.07023

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Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening

Saha, S. K., Fernando, B., Cuadros, J., Xiao, D., & Kanagasingam, Y. (2017). Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening. Retrieved from http://arxiv.org/abs/1703.02511

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Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields

Zhou, Y., Kneip, L., & Li, H. (2017). Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields. Retrieved from http://arxiv.org/abs/1702.02512

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Simultaneous Feature Aggregating and Hashing for Large-scale Image Search

Do, T.-T., Le, D.-K., Trung, T., & Pham, T. (n.d.). Simultaneous Feature Aggregating and Hashing for Large-scale Image Search. Retrieved from https://arxiv.org/pdf/1704.00860.pdf

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Adversarial Generation of Training Examples for Vehicle License Plate Recognition

Wang, X., You, M., & Shen, C. (2017). Adversarial Generation of Training Examples for Vehicle License Plate Recognition. Retrieved from http://arxiv.org/abs/1707.03124

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Robust Visual Tracking via Hierarchical Convolutional Features

*Ma, C., Huang, J.-B., Yang, X., & Yang, M.-H. (n.d.). Robust Visual Tracking via Hierarchical Convolutional Features. Retrieved from https://arxiv.org/pdf/1707.03816.pdf

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Visual Question Answering with Memory-Augmented Networks

Ma, C., Shen, C., Dick, A., & Van Den Hengel, A. (n.d.). Visual Question Answering with Memory-Augmented Networks. Retrieved from https://arxiv.org/pdf/1707.04968.pdf

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Coresets for Triangulation

Zhang, Q., & Chin, T.-J. (2017). Coresets for Triangulation. Retrieved from http://arxiv.org/abs/1707.05466

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Low-Rank Kernel Subspace Clustering

Ji, P., Reid, I., Garg, R., Li, H., & Salzmann, M. (2017). Low-Rank Kernel Subspace Clustering. Retrieved from http://arxiv.org/abs/1707.04974

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Maximizing rigidity revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views

Ji, P., Li, H., Dai, Y., & Reid, I. (2017). Maximizing rigidity revisited: a convex programming approach for generic 3D shape reconstruction from multiple perspective views. Retrieved from http://arxiv.org/abs/1707.05009

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Visually Aligned Word Embeddings for Improving Zero-shot Learning

Qiao, R., Liu, L., Shen, C., & Hengel, A. van den. (2017). Visually Aligned Word Embeddings for Improving Zero-shot Learning. Retrieved from http://arxiv.org/abs/1707.05427

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Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

Dharmasiri, T., Spek, A., & Drummond, T. (2017). Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs. Retrieved from https://arxiv.org/abs/1706.07593

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Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization

Garg, S., Jacobson, A., Kumar, S., & Milford, M. (2017). Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization. Retrieved from http://arxiv.org/abs/1706.07144

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Long Range Iris Recognition: A Survey

Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., & Ross, A. (2017). Long Range Iris Recognition: A Survey. Pattern Recognition. http://doi.org/10.1016/j.patcog.2017.05.021 *In Press

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Joint Pose and Principal Curvature Refinement Using Quadrics

Spek, A., & Drummond, T. (2017). Joint Pose and Principal Curvature Refinement Using Quadrics. Retrieved from http://arxiv.org/abs/1707.00381

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Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features

Shigematsu, R., Feng, D., You, S., & Barnes, N. (2017). Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features. Retrieved from https://arxiv.org/pdf/1705.03607.pdf

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3D tracking of water hazards with polarized stereo cameras

Nguyen, C. V., Milford, M., & Mahony, R. (2017). 3D tracking of water hazards with polarized stereo cameras. Retrieved from http://arxiv.org/abs/1701.04175

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Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

Zhang, F., Leitner, J., Milford, M., & Corke, P. I. (2017). Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination. Retrieved from http://arxiv.org/abs/1705.05116

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Sequence Summarization Using Order-constrained Kernelized Feature Subspaces

Cherian, A., Sra, S., & Hartley, R. (2017). Sequence Summarization Using Order-constrained Kernelized Feature Subspaces. Retrieved from https://arxiv.org/pdf/1705.08583.pdf

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Weakly Supervised Semantic Segmentation Based on Co-segmentation

Shen, T., Lin, G., Liu, L., Shen, C., & Reid, I. (2017). Weakly Supervised Semantic Segmentation Based on Co-segmentation. Retrieved from http://arxiv.org/abs/1705.09052

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Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks

Meyer, B. J., Harwood, B., & Drummond, T. (2017). Nearest Neighbour Radial Basis Function Solvers for Deep Neural Networks. Retrieved from http://arxiv.org/abs/1705.09780

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Discriminatively Learned Hierarchical Rank Pooling Networks

Fernando, B., & Gould, S. (2017). Discriminatively Learned Hierarchical Rank Pooling Networks. Retrieved from http://arxiv.org/abs/1705.10420

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Care about you: towards large-scale human-centric visual relationship detection

Zhuang, B., Wu, Q., Shen, C., Reid, I., & Hengel, A. van den. (2017). Care about you: towards large-scale human-centric visual relationship detection. Retrieved from http://arxiv.org/abs/1705.09892

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Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

Leal-Taixé, L., Milan, A., Schindler, K., Cremers, D., Reid, I., & Roth, S. (2017). Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking. Retrieved from http://arxiv.org/abs/1704.02781

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Smart Mining for Deep Metric Learning

Kumar, V. B. G., Harwood, B., Carneiro, G., Reid, I., & Drummond, T. (2017). Smart Mining for Deep Metric Learning. Retrieved from http://arxiv.org/abs/1704.01285

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Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems

Molloy, Timothy L., Ford, Jason J., & Mejias, L. (2017). Detection of Aircraft Below The Horizon for Vision-Based Detect And Avoid in Unmanned Aircraft Systems. Journal of Field Robotics. http://doi.org/10.1002/rob.21719

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Inverse noncooperative dynamic games

Molloy, T. L., Ford, J. J., & Perez, T. (2017). Inverse noncooperative dynamic games. In 20th World Congress of the International Federation of Automatic Control (IFAC 2017). Toulouse, France. Retrieved from http://eprints.qut.edu.au/105144/

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What Would You Do? Acting by Learning to Predict

Tow, A., Sünderhauf, N., Shirazi, S., Milford, M., & Leitner, J. (2017). What Would You Do? Acting by Learning to Predict. Retrieved from http://arxiv.org/abs/1703.02658

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Episode-Based Active Learning with Bayesian Neural Networks

Dayoub, F., Sünderhauf, N., & Corke, P. (2017). Episode-Based Active Learning with Bayesian Neural Networks. Retrieved from http://arxiv.org/abs/1703.07473

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Towards Unsupervised Weed Scouting for Agricultural Robotics

Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards Unsupervised Weed Scouting for Agricultural Robotics. Retrieved from http://arxiv.org/abs/1702.01247

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3D tracking of water hazards with polarized stereo cameras

Nguyen, C. V., Milford, M., & Mahony, R. (2017). 3D tracking of water hazards with polarized stereo cameras. Retrieved from http://arxiv.org/abs/1701.04175

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Action Recognition: From Static Datasets to Moving Robots

Rezazadegan, F., Shirazi, S., Upcroft, B., & Milford, M. (2017). Action Recognition: From Static Datasets to Moving Robots. Retrieved from http://arxiv.org/abs/1701.04925

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Deep Learning Features at Scale for Visual Place Recognition

Chen, Z., Jacobson, A., Sunderhauf, N., Upcroft, B., Liu, L., Shen, C., Reid, I., Milford, M. (2017). Deep Learning Features at Scale for Visual Place Recognition. Retrieved from http://arxiv.org/abs/1701.05105

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