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Scientific Publications

2020All Categories [81]

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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Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function

Khatun, A., Denman, S., Sridharan, S., & Fookes, C. (2020). Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function. Computer Vision and Image Understanding, 102989. https://doi.org/10.1016/j.cviu.2020.102989

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Autonomous UAV Navigation for Active Perception of Targets in Uncertain and Cluttered Environments

Sandino, J., Vanegas, F., Gonzalez, F., & Maire, F. (2020). Autonomous UAV Navigation for active perception of targets in uncertain and cluttered environments. Proceedings of 2020 IEEE Aerospace Conference.

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Special Issue on Deep Learning for Robotic Vision

Angelova, A., Carneiro, G., Sünderhauf, N., & Leitner, J. (2020, May 1). Special Issue on Deep Learning for Robotic Vision. International Journal of Computer Vision. https://doi.org/10.1007/s11263-020-01324-z

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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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Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017

Leitner, J., Morrison, D., Milan, A., Kelly-Boxall, N., McTaggart, M., Tow, A. W., & Corke, P. (2020). Designing Cartman: A Cartesian Manipulator for the Amazon Robotics Challenge 2017. In Advances on Robotic Item Picking (pp. 125–148). https://doi.org/10.1007/978-3-030-35679-8_11

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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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Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents

Law, E., Ravari, P. B., Chhibber, N., Kulic, D., Lin, S., Pantasdo, K. D., Ceha, J., Suh, S., & Dillen, N. (2020). Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents. https://doi.org/10.1145/3334480.3382783

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Haptics in Teleoperated Medical Interventions: Force Measurement, Haptic Interfaces and their Influence on User’s Performance

Abdi, E., Kulic, D., & Croft, E. (2020). Haptics in teleoperated medical interventions: Force measurement, haptic interfaces and their influence on users performance. IEEE Transactions on Biomedical Engineering, 1–1. https://doi.org/10.1109/tbme.2020.2987603

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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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Context from within: Hierarchical context modeling for semantic segmentation

Nguyen, K., Fookes, C., & Sridharan, S. (2020). Context from within: Hierarchical context modeling for semantic segmentation. Pattern Recognition, 105, 107358. https://doi.org/10.1016/j.patcog.2020.107358

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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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Attention Driven Fusion for Multi-Modal Emotion Recognition

Priyasad, D., Fernando, T., Denman, S., Sridharan, S., & Fookes, C. (2020, April 9). Attention Driven Fusion for Multi-Modal Emotion Recognition. 3227–3231. https://doi.org/10.1109/icassp40776.2020.9054441

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Neural Memory Plasticity for Medical Anomaly Detection

Fernando, T., Denman, S., Ahmedt-Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2020). Neural memory plasticity for medical anomaly detection. Neural Networks, 127, 67–81. https://doi.org/10.1016/j.neunet.2020.04.011

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Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses

Ahmedt Aristizabal, D., Fernando, T., Denman, S., Robinson, J. E., Sridharan, S., Johnston, P. J., Laurens, K.R., & Fookes, C. (2020). Identification of Children At Risk of Schizophrenia via Deep Learning and EEG Responses. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/JBHI.2020.2984238

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Bio-inspired multi-scale fusion

Hausler, S., Chen, Z., Hasselmo, M. E., & Milford, M. (2020). Bio-inspired multi-scale fusion. Biological Cybernetics, 114(2), 209–229. https://doi.org/10.1007/s00422-020-00831-z

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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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Low-cost sensors as an alternative for long-term air quality monitoring

Liu, X., Jayaratne, R., Thai, P., Kuhn, T., Zing, I., Christensen, B., Lamont, R., Dunbabin, M., Zhu, S., Gao, J., Wainwright, D., Neale, D., Kan, R., Kirkwood, J., & Morawska, L. (2020). Low-cost sensors as an alternative for long-term air quality monitoring. Environmental Research, 185, 109438. https://doi.org/10.1016/j.envres.2020.109438

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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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A Software System for Human-Robot Interaction To Collect Research Data: A HTML/Javascript Service on the Pepper Robot

Suddrey, G., & Robinson, N. (2020). A Software System for Human-Robot Interaction To Collect Research Data. Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 459–461. https://doi.org/10.1145/3371382.3378287

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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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High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks

Pfister, F. M. J., Um, T. T., Pichler, D. C., Goschenhofer, J., Abedinpour, K., Lang, M., Endo, S., Ceballos-Baumann, A. O., Hirche, S., Bischl, B., Kulić, D., & Fietzek, U. M. (2020). High-Resolution Motor State Detection in Parkinson’s Disease Using Convolutional Neural Networks. Scientific Reports, 10(1), 5860. https://doi.org/10.1038/s41598-020-61789-3

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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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Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy

"Gustavo Carneiro, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Alastair Burt, Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy, Medical Image Analysis,Volume 62,2020,101653,ISSN 1361-8415,https://doi.org/10.1016/j.media.2020.101653."

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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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On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering

Wang, X., Liu, Y., Shen, C., Ng, C. C., Luo, C., Jin, L., Chan, C. S., van den Hengel, A., & Wang, L. (2020). On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering. Retrieved from http://arxiv.org/abs/2002.10215

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Learning Deep Gradient Descent Optimization for Image Deconvolution

Gong, D., Zhang, Z., Shi, Q., van den Hengel, A., Shen, C., & Zhang, Y. (2020). Learning Deep Gradient Descent Optimization for Image Deconvolution. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.2968289

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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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BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., & Yan, Y. (2020). BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation. Retrieved from http://arxiv.org/abs/2001.00309

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Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images

Dunnhofer, M., Antico, M., Sasazawa, F., Takeda, Y., Camps, S., Martinel, N., Micheloni, C., Carneiro, G., & Fontanarosa, D. (2020). Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Medical Image Analysis, 60. https://doi.org/10.1016/j.media.2019.101631

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Fast Image Reconstruction with an Event Camera

Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N., Mahony, R. E., & Scaramuzza, D. (2020). Fast Image Reconstruction with an Event Camera. Retrieved from https://github.com/uzh-rpg/rpg

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Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization

Hua, M. D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2020). Nonlinear observer design on SL(3) for homography estimation by exploiting point and line correspondences with application to image stabilization. Automatica, 115, 1–10.

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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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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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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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