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Publications

2016 Submitted [32]

Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data

*Zhuang, B., Liu, L., Li, Y., Shen, C., & Reid, I. (2016). Attend in groups: a weakly-supervised deep learning framework for learning from web data. Retrieved from https://arxiv.org/abs/1611.09960

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Not All Negatives are Equal: Learning to Track with Multiple Background Clusters

*Zhu, G., Porikli, F., & Li, H. (2016). Not All Negatives are Equal: Learning to Track with Multiple Background Clusters. IEEE Transactions on Circuits and Systems for Video Technology, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TCSVT.2016.2615518

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Neural Aggregation Network for Video Face Recognition

*Yang, J., Ren, P., Chen, D., Wen, F., Li, H., & Hua, G. (2016). Neural Aggregation Network for Video Face Recognition. Retrieved from http://arxiv.org/abs/1603.05474

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Automatic Event Detection for Signal-based Surveillance

Xu, J., Fookes, C., & Sridharan, S. (2016). Automatic Event Detection for Signal-based Surveillance. Retrieved from http://arxiv.org/abs/1612.01611

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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

*Wu, Z., Shen, C., & Hengel, A. van den. (2016). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Retrieved from http://arxiv.org/abs/1611.10080

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The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

*Wang, P., Wu, Q., Shen, C., & Van Den Hengel, A. (n.d.). The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions. Retrieved from https://arxiv.org/abs/1612.05386

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Hi Detector, What’s Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution

*Wang, P., Liu, L., Shen, C., Hengel, A. van den, & Shen, H. T. (2016). Hi Detector, What’s Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution. Retrieved from http://arxiv.org/abs/1602.04422

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Mirrored Light Field Video Camera Adapter

*Tsai, D., Dansereau, D. G., Martin, S., & Corke, P. (2016). Mirrored Light Field Video Camera Adapter. Retrieved from http://arxiv.org/abs/1612.05335

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Graph-Structured Representations for Visual Question Answering

*Teney, D., Liu, L., & Hengel, A. van den. (2016). Graph-Structured Representations for Visual Question Answering. Retrieved from http://arxiv.org/abs/1609.05600

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Meaningful Maps $-$ Object-Oriented Semantic Mapping

*Sünderhauf, N., Pham, T. T., Latif, Y., Milford, M., & Reid, I. (2016). Meaningful Maps $-$ Object-Oriented Semantic Mapping. Retrieved from http://arxiv.org/abs/1609.07849

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DeepSetNet: Predicting Sets with Deep Neural Networks

*Rezatofighi, S. H., G, V. K. B., Milan, A., Abbasnejad, E., Dick, A., & Reid, I. (2016). DeepSetNet: Predicting Sets with Deep Neural Networks. Retrieved from http://arxiv.org/abs/1611.08998

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Clustering with Hypergraphs: The Case for Large Hyperedges

*Purkait, P., Chin, T.-J., Sadri, A., & Suter, D. (2016). Clustering with Hypergraphs: The Case for Large Hyperedges. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TPAMI.2016.2614980

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MOT16: A Benchmark for Multi-Object Tracking. Retrieved

*Milan, A., Leal-Taixe, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A Benchmark for Multi-Object Tracking. Retrieved from http://arxiv.org/abs/1603.00831

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Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments

*Liu, L., Li, H., & Dai, Y. (2016). Robust and Efficient Relative Pose with a Multi-camera System for Autonomous Vehicle in Highly Dynamic Environments. Retrieved from http://arxiv.org/abs/1605.03689

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Exploring Context with Deep Structured models for Semantic Segmentation

*Lin, G., Shen, C., Hengel, A. van den, & Reid, I. (2016). Exploring Context with Deep Structured models for Semantic Segmentation. Retrieved from http://arxiv.org/abs/1603.03183

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RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

*Lin, G., Milan, A., Shen, C., & Reid, I. (2016). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation. Retrieved from http://arxiv.org/abs/1611.06612

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Sequential Person Recognition in Photo Albums with a Recurrent Network

*Li, Y., Lin, G., Zhuang, B., Liu, L., Shen, C., & Hengel, A. van den. (2016). Sequential Person Recognition in Photo Albums with a Recurrent Network. Retrieved from http://arxiv.org/abs/1611.09967

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The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research

*Leitner, J., Tow, A. W., Dean, J. E., Suenderhauf, N., Durham, J. W., Cooper, M., et al. (2016). The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research. Retrieved from http://arxiv.org/abs/1609.05258

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

Harandi, M., Salzmann, M., & Hartley, R. (2016). Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods. Retrieved from http://arxiv.org/abs/1605.06182

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Generalized BackPropagation Etude De Cas: Orthogonality

*Harandi, M., & Fernando, B. (2016). Generalized BackPropagation, ’{E}tude De Cas: Orthogonality. Retrieved from http://arxiv.org/abs/1611.05927

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On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

Gould, S., Fernando, B., Cherian, A., Anderson, P., Cruz, R. S., & Guo, E. (2016). On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization. Retrieved from http://arxiv.org/abs/1607.05447

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Unsupervised Human Action Detection by Action Matching

*Fernando, B., Shirazi, S., & Gould, S. (2016). Unsupervised Human Action Detection by Action Matching. Retrieved from http://arxiv.org/abs/1612.00558

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Rank Pooling for Action Recognition

*Fernando, B., Gavves, E., Oramas, J., Ghodrati, A., & Tuytelaars, T. (2016). Rank Pooling for Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP (99), 1–1. Retrieved from http://doi.org/10.1109/TPAMI.2016.2558148

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ARTiS: Appearance-based Action Recognition in Task Space

* Eich, M., Shirazi, S., & Wyeth, G. (2016). ARTiS: Appearance-based Action Recognition in Task Space. Retrieved from https://arxiv.org/abs/1610.05432

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Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters

*Drory, A., Zhu, G., Li, H., & Hartley, R. (2016). Automated detection and tracking of slalom paddlers from broadcast image sequences using cascade classifiers and discriminative correlation filters. Computer Vision and Image Understanding. Retrieved from http://doi.org/10.1016/j.cviu.2016.12.002

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Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation

*Cao, Y., Shen, C., & Shen, H. T. (2016). Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation. Retrieved from http://arxiv.org/abs/1610.01706

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Advances in Visual Computing

* Bebis, G., Boyle, R., Parvin, et al. (2016). Advances in Visual Computing. (Vol. 10072). Cham: Springer International Publishing. Retrieved from http://doi.org/10.1007/978-3-319-50835-1

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Aerodynamics of Rotor Blades for Quadrotors

Bangura, M., Melega, M., Naldi, R., & Mahony, R. (2016). Aerodynamics of Rotor Blades for Quadrotors. Retrieved from http://arxiv.org/abs/1601.00733

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Guided Open Vocabulary Image Captioning with Constrained Beam Search

*Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016). Guided Open Vocabulary Image Captioning with Constrained Beam Search. Retrieved from http://arxiv.org/abs/1612.00576

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Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation

*Aliakbarian, M. S., Saleh, F., Fernando, B., Salzmann, M., Petersson, L., & Andersson, L. (2016). Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation. Retrieved from http://arxiv.org/abs/1611.05520

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Spherepix: a Data Structure for Spherical Image Processing

*Adarve, J. D., & Mahony, R. (2016). Spherepix: a Data Structure for Spherical Image Processing. IEEE Robotics and Automation Letters, PP (99), 1–1. Retrieved from http://doi.org/10.1109/LRA.2016.2645119

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Infinite Variational Autoencoder for Semi-Supervised Learning

*Abbasnejad, E., Dick, A., & Hengel, A. van den. (2016). Infinite Variational Autoencoder for Semi-Supervised Learning. Retrieved from http://arxiv.org/abs/1611.07800

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