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2020 All Categories [35]

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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Low-cost PM2. 5 Sensors: An Assessment of Their Suitability for Various Applications

Jayaratne, R., Liu, X., Ahn, K.-H., Asumadu-Sakyi, A., Fisher, G., Gao, J., Mabon, A., Mazaheri, M., Mullins, B., Nyaku, M., Ristovki, Z., Scorgie, Y., Thai, P., Dunbabin, M., & Morawska, L. (2020). Low-cost PM 2.5 Sensors: An Assessment of their Suitability for Various Applications. Aerosol and Air Quality Research, 20, 520–532. https://doi.org/10.4209/aaqr.2018.10.0390

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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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Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats

Sekar, S., Panchal, S. K., Ghattamaneni, N. K., Brown, L., Crawford, R., Xiao, Y., & Prasadam, I. (2020). Dietary Saturated Fatty Acids Modulate Pain Behaviour in Trauma-Induced Osteoarthritis in Rats. Nutrients, 12(2), 509. https://doi.org/10.3390/nu12020509

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Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning

Jonmohamadi, Y., Takeda, Y., Liu, F., Sasazawa, F., Maicas, G., Crawford, R., Roberts, J., Pandey, A.K., & Carneiro, G. (2020). Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.2980025

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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Schaffter, T., Buist, D. S. M., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., … Jung, H. (2020). Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Network Open, 3(3), e200265. https://doi.org/10.1001/jamanetworkopen.2020.0265

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LSTM guided ensemble correlation filter tracking with appearance model pool

Jain, M., Subramanyam, A. V., Denman, S., Sridharan, S., & Fookes, C. (2020). LSTM guided ensemble correlation filter tracking with appearance model pool. Computer Vision and Image Understanding, 195, 102935. https://doi.org/10.1016/j.cviu.2020.102935

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Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification.

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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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A Hybrid Compact Neural Architecture for Visual Place Recognition

Chancan, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2020). A Hybrid compact neural architecture for visual place recognition. IEEE Robotics and Automation Letters, 5(2), 993–1000. https://doi.org/10.1109/LRA.2020.2967324

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Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M. J., & McDonald-Maier, K. D. (2020). Exploring performance bounds of visual place recognition using extended precision. IEEE Robotics and Automation Letters, 5(2), 1688–1695. https://doi.org/10.1109/LRA.2020.2969197

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CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

Zaffar, M., Ehsan, S., Milford, M., & McDonald-Maier, K. (2020). CoHOG: A light-weight, compute-efficient, and training-free visual place recognition technique for changing environments. IEEE Robotics and Automation Letters, 5(2), 1835–1842. https://doi.org/10.1109/LRA.2020.2969917

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Model-free vision-based shaping of deformable plastic materials

Cherubini, A., Ortenzi, V., Cosgun, A., Lee, R., & Corke, P. (2020). Model-free vision-based shaping of deformable plastic materials. The International Journal of Robotics Research, 027836492090768. https://doi.org/10.1177/0278364920907684

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Hierarchical Attention Network for Action Segmentation

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2020). Hierarchical Attention Network for Action Segmentation. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2020.01.023

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Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park, C., Moghadam, P., Kim, S., Sridharan, S., & Fookes, C. (2020). Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach. IEEE Robotics and Automation Letters, 1–1. https://doi.org/10.1109/LRA.2020.2969164

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Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study

Vickers, M. L., Ballard, E. L., Harris, P. N. A., Knibbs, L. D., Jaiprakash, A., Dulhunty, J. M., … Parkinson, B. (2020). Bacterial Profile, Multi-Drug Resistance and Seasonality Following Lower Limb Orthopaedic Surgery in Tropical and Subtropical Australian Hospitals: An Epidemiological Cohort Study. International Journal of Environmental Research and Public Health, 17(2), 657. https://doi.org/10.3390/ijerph17020657

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Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy

Antico, M., Fontanarosa, D., Carneiro, G., Vukovic, D., Camps, S. M., Sasazawa, F., … Crawford, R. (2020). Deep learning for US image quality assessment based on femoral cartilage boundaries detection in autonomous knee arthroscopy. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. https://doi.org/10.1109/TUFFC.2020.2965291

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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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Learning landmark guided embeddings for animal re-identification

Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning landmark guided embeddings for animal re-identification. Retrieved from http://arxiv.org/abs/2001.02801

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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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Towards Surgical Robots: Understanding Interaction Challenges in Knee Surgery

Opie, J., Jaiprakash, A., Ploderer, B., Brereton, M., & Roberts, J. (2019). Towards Surgical Robots. Proceedings of the 31st Australian Conference on Human-Computer-Interaction, 255–265. https://doi.org/10.1145/3369457.3370916

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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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A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Hinas, A., Ragel, R., Roberts, J., & Gonzalez, F. (2020). A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS. Sensors, 20(1), 272. https://doi.org/10.3390/s20010272

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Architecture Search of Dynamic Cells for Semantic Video Segmentation

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2020). Architecture Search of Dynamic Cells for Semantic Video Segmentation.

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Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Nekrasov, V., Shen, C., & Reid, I. (2020). Template-Based Automatic Search of Compact Semantic Segmentation Architectures.

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