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Publications

2020 Submitted [16]

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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Traversing the Reality Gap via Simulator Tuning

Collins, J., Brown, R., Leitner, J., & Howard, D. (2020). Traversing the Reality Gap via Simulator Tuning. Retrieved from http://arxiv.org/abs/2003.01369

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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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What can robotics research learn from computer vision research?

Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2020). What can robotics research learn from computer vision research? Retrieved from http://arxiv.org/abs/2001.02366

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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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