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Publications

2020 Submitted [41]

Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection

Dissanayake, T., Fernando, T., Denman, S., Sridharan, S., Ghaemmaghami, H., & Fookes, C. (2020). Understanding the Importance of Heart Sound Segmentation for Heart Anomaly Detection. Retrieved from http://arxiv.org/abs/2005.10480

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

Zaffar, M., Ehsan, S., Milford, M., Flynn, D., & McDonald-Maier, K. (2020). VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change. Retrieved from http://arxiv.org/abs/2005.08135

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

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

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Active Preference Learning using Maximum Regret

Wilde, N., Kulic, D., & Smith, S. L. (2020). Active Preference Learning using Maximum Regret.

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Supportive Actions for Manipulation in Human-Robot Coworker Teams

Bansal, S., Newbury, R., Chan, W., Cosgun, A., Allen, A., Kulić, D., Drummond, D., & Isbell, C. (2020). Supportive Actions for Manipulation in Human-Robot Coworker Teams. Retrieved from http://arxiv.org/abs/2005.00769

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End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification. Retrieved from http://arxiv.org/abs/2005.03222

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

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

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

Miller, D., Sünderhauf, N., Milford, M., & Dayoub, F. (2020). Class Anchor Clustering: a Distance-based Loss for Training Open Set Classifiers. Retrieved from http://arxiv.org/abs/2004.02434

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

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

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Joint Deep Cross-Domain Transfer Learning for Emotion Recognition

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

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How to Train Your Event Camera Neural Network

Stoffregen, T., Scheerlinck, C., Scaramuzza, D., Drummond, T., Barnes, N., Kleeman, L., & Mahony, R. (2020). How to Train Your Event Camera Neural Network. Retrieved from http://arxiv.org/abs/2003.09078

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

Ben-Shabat, Y., & Gould, S. (2020). DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares. Retrieved from http://arxiv.org/abs/2003.10826

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Generative Low-bitwidth Data Free Quantization

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

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Efficient Semantic Video Segmentation with Per-frame Inference

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

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PAC-Bayesian Meta-learning with Implicit Prior

Nguyen, C., Do, T.-T., & Carneiro, G. (2020). PAC-Bayesian Meta-learning with Implicit Prior. Retrieved from http://arxiv.org/abs/2003.02455

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

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

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Globally Optimal Contrast Maximisation for Event-based Motion Estimation

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

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3D Gated Recurrent Fusion for Semantic Scene Completion

Liu, Y., Li, J., Yan, Q., Yuan, X., Zhao, C., Reid, I., & Cadena, C. (2020). 3D Gated Recurrent Fusion for Semantic Scene Completion. Retrieved from http://arxiv.org/abs/2002.07269

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

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

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DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data

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

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Learn to Predict Sets Using Feed-Forward Neural Networks

Rezatofighi, H., Kaskman, R., Motlagh, F. T., Shi, Q., Milan, A., Cremers, D., Leal-Taixé, L., & Reid, I. (2020). Learn to Predict Sets Using Feed-Forward Neural Networks. Retrieved from http://arxiv.org/abs/2001.11845

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

Luo, C., Lin, Q., Liu, Y., Jin, L., & Shen, C. (2020). Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild. Retrieved from http://arxiv.org/abs/2001.04189

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

Zhang, X., Gong, D., Cao, J., & Shen, C. (2020). Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory. Retrieved from http://arxiv.org/abs/2001.04123

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

Xiong, H., Lu, H., Liu, C., Liu, L., Shen, C., & Cao, Z. (2020). From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting. Retrieved from https://tinyurl.com/SS-DCNet.

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

He, T., Gong, D., Tian, Z., & Shen, C. (2020). Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation.

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

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

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Discrimination-aware Network Pruning for Deep Model Compression

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

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Switchable Precision Neural Networks

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

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Automatic Pruning for Quantized Neural Networks

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

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OpenGAN: Open Set Generative Adversarial Networks

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

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String stable integral control of vehicle platoons with disturbances

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

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A Multiple Decoder CNN for Inverse Consistent 3D Image Registration

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

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Enhancing Feature Invariance with Learned Image Transformations for Image Retrieval

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

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

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

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Maximising Manipulability During Resolved-Rate Motion Control

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

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Robot Navigation in Unseen Spaces using an Abstract Map

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

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

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

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

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

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Hierarchical Multi-Process Fusion for Visual Place Recognition

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

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

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

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

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

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Australian Centre for Robotic Vision
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