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Scientific Publications

2019 All Categories [253]

Deep Hashing by Discriminating Hard Examples

Yan, C., Pang, G., Bai, X., Shen, C., Zhou, J., Hancock, E., & Yang, C. (2019). Deep Hashing by Discriminating Hard Examples. https://doi.org/10.1145/3343031.3350927

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New Convex Relaxations for MRF Inference with Unknown Graphs

Wang, Z., Liu, T., Shi, Q., Pawan Kumar, M., & Zhang, J. (2019). New Convex Relaxations for MRF Inference with Unknown Graphs.

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Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks

Yu, X., Porikli, F., Fernando, B., & Hartley, R. (2019). Hallucinating Unaligned Face Images by Multiscale Transformative Discriminative Networks. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01254-5

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Real Image Denoising with Feature Attention

Answar, S., & Barnes, N. (2019). Supplementary: Real Image Denoising with Feature Attention. Retrieved from http://arxiv.org/abs/1807.04686

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Learning to Find Common Objects Across Few Image Collections

Shaban, A., Rahimi, A., Bansal, S., Gould, S., Boots, B., & Hartley, R. (2019). Learning to Find Common Objects Across Few Image Collections. Retrieved from http://arxiv.org/abs/1904.12936

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Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison

Li, D., Opazo, C. R., Yu, X., & Li, H. (2019). Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison. Retrieved from http://arxiv.org/abs/1910.11006

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Bilinear Attention Networks for Person Retrieval

Fang, P., Zhou, J., Kumar Roy, S., Petersson, L., & Harandi, M. (2019). Bilinear Attention Networks for Person Retrieval.

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Siamese Networks: The Tale of Two Manifolds

Kumar Roy, S., Harandi, M., Nock, R., & Hartley, R. (n.d.). Siamese Networks: The Tale of Two Manifolds. Retrieved from https://github.com/sumo8291/

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A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing

Korthals, T., Schilling, M., & Leitner, J. (2019). A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing. Retrieved from http://arxiv.org/abs/1911.00584

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Benchmarking Simulated Robotic Manipulation through a Real World Dataset

Collins, J., McVicar, J., Wedlock, D., Brown, R., Howard, D., & Leitner, J. (2019). Benchmarking Simulated Robotic Manipulation through a Real World Dataset. Retrieved from http://arxiv.org/abs/1911.01557

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Predicting the Future: A Jointly Learnt Model for Action Anticipation

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Predicting the Future: A Jointly Learnt Model for Action Anticipation.

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Heart Sound Segmentation using Bidirectional LSTMs with Attention

Fernando, T., Ghaemmaghami, H., Denman, S., Sridharan, S., Hussain, N., & Fookes, C. (2019). Heart Sound Segmentation using Bidirectional LSTMs with Attention. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/JBHI.2019.2949516

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Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost

Le, H., Xu, M., Hoang, T., & Milford, M. (2019). Hierarchical Encoding of Sequential Data with Compact and Sub-linear Storage Cost.

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JRDB: A Dataset and Benchmark for Visual Perception for Navigation in Human Environments

Martín-Martín, R., Rezatofighi, H., Shenoi, A., Patel, M., Gwak, J., Dass, N., Federman, A., Goebel, P., Savarese, S. (2019). JRDB: A Dataset and Benchmark for Visual Perception for Navigation in Human Environments. Retrieved from http://svl.stanford.edu/projects/jackrabbot/

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Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning

Pang, G., Hengel, A. van den, & Shen, C. (2019). Weakly-supervised Deep Anomaly Detection with Pairwise Relation Learning. Retrieved from www.aaai.org

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Watch, Reason and Code: Learning to Represent Videos Using Program

Duan, X., Wu, Q., Gan, C., Zhang, Y., Huang, W., van den Hengel, A., & Zhu, W. (2019). Watch, Reason and Code. In Proceedings of the 27th ACM International Conference on Multimedia - MM ’19 (pp. 1543–1551). New York, New York, USA: ACM Press. https://doi.org/10.1145/3343031.3351094

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Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

Glaser, S., Maicas, G., Bedrikovetski, S., Sammour, T., & Carneiro, G. (2019). Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT. Retrieved from http://arxiv.org/abs/1910.10371

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Photoshopping Colonoscopy Video Frames

Liu, Y., Tian, Y., Maicas, G., Pu, L. Z. C. T., Singh, R., Verjans, J. W., & Carneiro, G. (2019). Photoshopping Colonoscopy Video Frames. Retrieved from http://arxiv.org/abs/1910.10345

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Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression

Cai, M., Zhan, H., Saroj Weerasekera, C., Li, K., & Reid, I. (2019). Camera Relocalization by Exploiting Multi-View Constraints for Scene Coordinates Regression. http://openaccess.thecvf.com/content_ICCVW_2019/papers/DL4VSLAM/Cai_Camera_Relocalization_by_Exploiting_Multi-View_Constraints_for_Scene_Coordinates_Regression_ICCVW_2019_paper

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Fast and Differentiable Message Passing for Stereo Vision

Xu, Z., Ajanthan, T., & Hartley, R. (2019). Fast and Differentiable Message Passing for Stereo Vision. Retrieved from http://arxiv.org/abs/1910.10892

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Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene

Li, L., Khan, S., & Barnes ANU, N. (2019). Silhouette-Assisted 3D Object Instance Reconstruction from a Cluttered Scene.

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Mirror Descent View for Neural Network Quantization

Ajanthan, T., Gupta, K., Torr, P. H. S., Hartley, R., & Dokania, P. K. (2019). Mirror Descent View for Neural Network Quantization. Retrieved from http://arxiv.org/abs/1910.08237

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Learning Trajectory Dependencies for Human Motion Prediction

Mao, W., Liu, M., Salzmann, M., & Li, H. (2019). Learning Trajectory Dependencies for Human Motion Prediction. Retrieved from http://arxiv.org/abs/1908.05436

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Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network

Shoeiby, M., Aliakbarian, S., Anwar, S., & Petersson, L. (2019). Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network. Retrieved from http://arxiv.org/abs/1909.07577

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Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency

Faisal, M., Akhter, I., Ali, M., & Hartley, R. (2019). Exploiting Geometric Constraints on Dense Trajectories for Motion Saliency. Retrieved from http://arxiv.org/abs/1909.13258

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CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation

Gupta, K., Petersson, L., & Hartley, R. (2019). CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation. Retrieved from http://arxiv.org/abs/1909.13476

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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation

Pan, L., Dai, Y., Liu, M., Porikli, F., & Pan, Q. (2019). Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation. Retrieved from http://arxiv.org/abs/1910.02442

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Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss

Yu, X., Tian, Y., Porikli, F., Hartley, R., Li, H., Heijnen, H., & Balntas, V. (2019). Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss.

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Unsupervised Primitive Discovery for Improved 3D Generative Modeling

Khan, S. H., Guo, Y., Hayat, M., & Barnes, N. (2019). Unsupervised Primitive Discovery for Improved 3D Generative Modeling. Retrieved from https://salman-h-khan.github.io/papers/CVPR19_2

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Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments

Rana, K., Talbot, B., Milford, M., & Sünderhauf, N. (2019). Residual Reactive Navigation: Combining Classical and Learned Navigation Strategies For Deployment in Unknown Environments. Retrieved from http://arxiv.org/abs/1909.10972

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Deep Segmentation-Emendation Model for Gland Instance Segmentation

Xie, Y., Lu, H., Zhang, J., Shen, C., & Xia, Y. (2019). Deep Segmentation-Emendation Model for Gland Instance Segmentation. https://doi.org/10.1007/978-3-030-32239-7_52

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Gradient Information Guided Deraining with A Novel Network and Adversarial Training

Wang, Y., Zhang, H., Liu, Y., Shi, Q., & Zeng, B. (2019). Gradient Information Guided Deraining with A Novel Network and Adversarial Training. Retrieved from http://arxiv.org/abs/1910.03839

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REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

Orlando, J. I., Fu, H., Barbossa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P-A., Kim, J., Lee, J., Lee, J., Li, X., Liu, P., Lu, S., Murugesan, B., Naranjo, V., Phaye, S S R., Shankaranarayana, S., Sikka, A., Son,J., van den Hengel, A., Wang, S., Wu, J., Wu, Z., Xu, G., Xu, Y., Yin, P., Li, F., Zhang, X., Yanwu, X., Bogunović, H. (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis, 59, 101570. https://doi.org/10.1016/j.media.2019.101570

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PolarMask: Single Shot Instance Segmentation with Polar Representation

Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Shen, C., Luo, P. (2019). PolarMask: Single Shot Instance Segmentation with Polar Representation. Retrieved from http://arxiv.org/abs/1909.13226

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A Generative Adversarial Density Estimator

Abbasnejad, M. E., Shi, J., Van Den Hengel, A., & Liu, L. (n.d.). A Generative Adversarial Density Estimator. _2019/papers/Abbasnejad_A_Generative_Adversarial_Density_Estimator_CVPR_2019_paper.pdf

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Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging

Oakden-Rayner, L., Dunnmon, J., Carneiro, G., & Ré, C. (2019). Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging. Retrieved from http://arxiv.org/abs/1909.12475

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Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Liu, Y., Liu, L., Zhang, H., Rezatofighi, H., & Reid, I. (2019). Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation. Retrieved from http://arxiv.org/abs/1909.13046

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Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints

Ch’ng, S.-F., Sogi, N., Purkait, P., Chin, T.-J., & Fukui, K. (2019). Resolving Marker Pose Ambiguity by Robust Rotation Averaging with Clique Constraints. Retrieved from http://arxiv.org/abs/1909.11888

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Structured Binary Neural Networks for Image Recognition

Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. (2019). Structured Binary Neural Networks for Image Recognition. Retrieved from http://arxiv.org/abs/1909.09934

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Visual Odometry Revisited: What Should Be Learnt?

Zhan, H., Weerasekera, C. S., Bian, J., & Reid, I. (2019). Visual Odometry Revisited: What Should Be Learnt? Retrieved from http://arxiv.org/abs/1909.09803

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IR-NAS: Neural Architecture Search for Image Restoration

Zhang, H., Li, Y., Chen, H., & Shen, C. (2019). IR-NAS: Neural Architecture Search for Image Restoration. Retrieved from http://arxiv.org/abs/1909.08228

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Part-Guided Attention Learning for Vehicle Re-Identification

Zhang, X., Zhang, R., Cao, J., Gong, D., You, M., & Shen, C. (2019). Part-Guided Attention Learning for Vehicle Re-Identification. Retrieved from http://arxiv.org/abs/1909.06023

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TextSR: Content-Aware Text Super-Resolution Guided by Recognition

Wang, W., Xie, E., Sun, P., Wang, W., Tian, L., Shen, C., & Luo, P. (2019). TextSR: Content-Aware Text Super-Resolution Guided by Recognition. Retrieved from http://arxiv.org/abs/1909.07113

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Task-Aware Monocular Depth Estimation for 3D Object Detection

Wang, X., Yin, W., Kong, T., Jiang, Y., Li, L., & Shen, C. (2019). Task-Aware Monocular Depth Estimation for 3D Object Detection. Retrieved from http://arxiv.org/abs/1909.07701

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Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation

Xing, Y., Ge, Z., Zeng, R., Mahapatra, D., Seah, J., Law, M., & Drummond, T. (2019). Adversarial Pulmonary Pathology Translation for Pairwise Chest X-Ray Data Augmentation. https://doi.org/10.1007/978-3-030-32226-7_84

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A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold

Gao, Z., Wu, Y., Harandi, M., & Jia, Y. (2019). A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2019.2939177

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Robotic and Image-Guided Knee Arthroscopy

Wu, L., Jaiprakash, A., Pandey, A. K., Fontanarosa, D., Jonmohamadi, Y., Antico, M., … Crawford, R. (2020). Robotic and Image-Guided Knee Arthroscopy. In Handbook of Robotic and Image-Guided Surgery (pp. 493–514). https://doi.org/10.1016/b978-0-12-814245-5.00029-3

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Neural Memory Plasticity for Anomaly Detection

Fernando, T., Denman, S., Ahmedt-Aristizabal, D., Sridharan, S., Laurens, K., Johnston, P., & Fookes, C. (2019). Neural Memory Plasticity for Anomaly Detection. Retrieved from http://arxiv.org/abs/1910.05448

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Forecasting Future Action Sequences with Neural Memory Networks

Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2019). Forecasting Future Action Sequences with Neural Memory Networks. Retrieved from http://arxiv.org/abs/1909.09278

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A Compact Neural Architecture for Visual Place Recognition

Chancán, M., Hernandez-Nunez, L., Narendra, A., Barron, A. B., & Milford, M. (2019). A Compact Neural Architecture for Visual Place Recognition. Retrieved from http://arxiv.org/abs/1910.06840

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From Visual Place Recognition to Navigation: Learning Sample-Efficient Control Policies across Diverse Real World Environments

Chancán, M., & Milford, M. (2019). From Visual Place Recognition to Navigation: Learning Sample-Efficient Control Policies across Diverse Real World Environments. Retrieved from http://arxiv.org/abs/1910.04335

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NeuroSLAM: a brain-inspired SLAM system for 3D environments

Yu, F., Shang, J., Hu, Y., & Milford, M. (2019). NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biological Cybernetics. https://doi.org/10.1007/s00422-019-00806-9

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CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition

Khaliq, A., Ehsan, S., Milford, M., & McDonald-Maier, K. (2019). CAMAL: Context-Aware Multi-scale Attention framework for Lightweight Visual Place Recognition. Retrieved from http://arxiv.org/abs/1909.08153

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Parallel Optimal Transport GAN

Avraham, G., Zuo, Y., & Drummond, T. (2019). Parallel Optimal Transport GAN *. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Avraham_Parallel_Optimal_Transport_GAN_CVPR_2019_paper.html

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Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI

Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., & Carneiro, G. (2019). Deep Reinforcement Learning for Detecting Breast Lesions from DCE-MRI (pp. 163–178). https://doi.org/10.1007/978-3-030-13969-8_8

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BPnP: Further Empowering End-to-End Learning with Back-Propagatable Geometric Optimization

Chen, B., Chin, T.-J., & Li, N. (2019). BPnP: Further Empowering End-to-End Learning with Back-Propagatable Geometric Optimization. Retrieved from http://arxiv.org/abs/1909.06043

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Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement

Chen, B., Cao, J., Parra, A., & Chin, T.-J. (2019). Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinement. Retrieved from http://arxiv.org/abs/1908.11542

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Training Compact Neural Networks via Auxiliary Overparameterization

Liu, Y., Zhuang, B., Shen, C., Chen, H., & Yin, W. (2019). Training Compact Neural Networks via Auxiliary Overparameterization. Retrieved from http://arxiv.org/abs/1909.02214

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From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Xiong, H., Lu, H., Liu, C., Liu, L., Cao, Z., & Shen, C. (2019). From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. Retrieved from https://github.

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Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network

Wang, W., Xie, E., Song, X., Zang, Y., Wang, W., Lu, T., … Shen, C. (2019). Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network. Retrieved from http://arxiv.org/abs/1908.05900

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MobileFAN: Transferring Deep Hidden Representation for Face Alignment

Zhao, Y., Liu, Y., Shen, C., Gao, Y., & Xiong, S. (2019). MobileFAN: Transferring Deep Hidden Representation for Face Alignment. Retrieved from http://arxiv.org/abs/1908.03839

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Exploiting temporal consistency for real-time video depth estimation

Zhang, H., Shen, C., Li, Y., Cao, Y., Liu, Y., & Yan, Y. (n.d.). Exploiting temporal consistency for real-time video depth estimation *. Retrieved from https://tinyurl.com/STCLSTM

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Evaluation of the impact of image spatial resolution in designing a context-based fully convolution neural networks for flood mapping

Sarker, C., Mejias, L., Maire, F. D., & Woodley, A. (2019). Evaluation of the impact of image spatial resolution in designing a context-based fully convolution neural networks for flood mapping.

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Where are the Keys?–Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks

Sünderhauf, N. (2019). Where are the Keys? -- Learning Object-Centric Navigation Policies on Semantic Maps with Graph Convolutional Networks. Retrieved from http://arxiv.org/abs/1909.07376

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A probabilistic challenge for object detection

Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443–443. https://doi.org/10.1038/s42256-019-0094-4

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Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery

Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

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Target-Specific Siamese Attention Network for Real-time Object Tracking

Hou, L., Chen, X., Lan, K., Rasmussen, R., & Roberts, J. (2019). Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing. IEEE Access, 7, 121949–121960. https://doi.org/10.1109/access.2019.2935547

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Volumetric Next Best View by 3D Occupancy Mapping Using Markov Chain Gibbs Sampler for Precise Manufacturing

Stanislas, L., Moyle, K., Corser, E., Ha, T., Dyson, R., Lamont, R., & Dunbabin, M. (2019). Bruce: A system-of-systems solution to the 2018 Maritime RobotX Challenge.

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Neighbourhood context embeddings in deep inverse reinforcement learning for predicting pedestrian motion over long time horizons

Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. Retrieved from http://arxiv.org/abs/1908.04930

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Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

Bian, J.-W., Li, Z., Wang, N., Zhan, H., Shen, C., Cheng, M.-M., & Reid, I. (2019). Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video. Retrieved from http://arxiv.org/abs/1908.10553

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Target-Aware Deep Tracking

Li, X., Ma, C., Wu, B., He, Z., & Yang, M.-H. (2019). Target-Aware Deep Tracking. Retrieved from http://arxiv.org/abs/1904.01772

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RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs

Liu, C., Ding, W., Xia, X., Hu, Y., Zhang, B., Liu, J., … Guo, G. (2019). RBCN: Rectified Binary Convolutional Networks for Enhancing the Performance of 1-bit DCNNs. Retrieved from http://arxiv.org/abs/1908.07748

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Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space

Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space. Retrieved from http://arxiv.org/abs/1908.04930

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Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations

Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, C. S. (2019). Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations. Retrieved from https://arxiv.org/pdf/1908.04680

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Indices Matter: Learning to Index for Deep Image Matting

Hao L, Yutong Dai, Chunhua Shen, S. X. (2019). Indices Matter: Learning to Index for Deep Image Matting. Retrieved from https://arxiv.org/pdf/1908.00672

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Enforcing geometric constraints of virtual normal for depth prediction

Wei Yin, Yifan Liu, Chunhua Shen, Y. Y. (2019). Enforcing geometric constraints of virtual normal for depth prediction. Retrieved from https://arxiv.org/pdf/1907.12209

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Few-Shot Meta-Denoising

Leslie Casas, Gustavo Carneiro, Nassir Navab, & and Vasileios Belagiannis. (2019). Few-Shot Meta-Denoising. Retrieved from https://arxiv.org/pdf/1908.00111

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Scalable Place Recognition Under Appearance Change for Autonomous Driving

Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin, Yu Liu, Thanh-Toan Do, & and Ian Reid. (2019). Scalable Place Recognition Under Appearance Change for Autonomous Driving. Retrieved from https://arxiv.org/pdf/1908.00178

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Residual Multiscale Based Single Image Deraining

Zheng, Y., Yu, X., Liu, M., & Zhang, S. (2019). YP.ZHENG ET AL: RESIDUAL MULTISCALE BASED SINGLE IMAGE DERAINING Residual Multiscale Based Single Image Deraining.

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Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes

Ramasinghe, S., Khan, S., Barnes, N., & Gould, S. (2019). Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes. Retrieved from http://arxiv.org/abs/1908.10209

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Proximal Mean-field for Neural Network Quantization

Ajanthan, T., Dokania, P. K., Hartley, R., & Torr, P. H. S. (2019). Proximal Mean-field for Neural Network Quantization. Retrieved from http://arxiv.org/abs/1812.04353

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Deep Declarative Networks: A New Hope

Gould, S., Hartley, R., & Campbell, D. (2019). Deep Declarative Networks: A New Hope. Retrieved from http://arxiv.org/abs/1909.04866

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Mind your neighbours: Image annotation with metadata neighbourhood graph co-attention networks

Zhang, J., Wu, Q., Zhang, J., Shen, C., & Lu, J. (2019). Mind Your Neighbours: Image Annotation with Metadata Neighbourhood Graph Co-Attention Networks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 2956-2964

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Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization

Gustavo Carneiro, João Manuel, R. S. Tavares, Andrew P. Bradley, João Paulo Papa, Jacinto C. Nascimento, Jaime S. Cardoso, Zhi Lu & Vasileios Belagiannis (2019) Editorial, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 7:3, 241-241, DOI: 10.1080/21681163.2019.1594056

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Multisensory Assisted In-hand Manipulation of Objects with a Dexterous Hand

Korthals, T., Melnik, A., Leitner, J., & Hesse, M. (n.d.). Multisensory Assisted In-hand Manipulation of Objects with a Dexterous Hand. Retrieved from http://arxiv.org/abs/1612.05424

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Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning

Stanislas, L., Nubert, J., Dugas, D., Nitsch, J., Sünderhauf, N., Siegwart, R., Cadena, C., Peynot, T. (2019). Airborne Particle Classification in LiDAR Point Clouds Using Deep Learning * indicates equal contributions. Retrieved from https://leo-stan.github.io/particles_detection_fsr

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Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation

Du, Y., Hetherington, N. J., Oon, C. L., Chan, W. P., Quintero, C. P., Croft, E., & Machiel Van der Loos, H. F. (2019). Group Surfing: A Pedestrian-Based Approach to Sidewalk Robot Navigation (pp. 6518–6524). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/icra.2019.8793608

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Curiosity Did Not Kill the Robot

Doering, M., Liu, P., Glas, D. F., Kanda, T., Kulić, D., & Ishiguro, H. (2019). Curiosity Did Not Kill the Robot. ACM Transactions on Human-Robot Interaction, 8(3), 1–24. https://doi.org/10.1145/3326462

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Ctrl-Z: Recovering from Instability in Reinforcement Learning

Dasagi, V., Bruce, J., Peynot, T., & Leitner, J. (2019). Ctrl-Z: Recovering from Instability in Reinforcement Learning. Retrieved from http://arxiv.org/abs/1910.03732

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TIMTAM: Tunnel-image texturally accorded mosaic for location refinement of underground vehicles with a single camera

Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). TIMTAM: Tunnel-Image Texturally Accorded Mosaic for Location Refinement of Underground Vehicles With a Single Camera. IEEE Robotics and Automation Letters, 4(4), 4362–4369.

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Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery

Strydom, M., Banach, A., Wu, L., Crawford, R., Roberts, J., & Jaiprakash, A. (2019). Real-time Joint Motion Analysis and Instrument Tracking for Robot-Assisted Orthopaedic Surgery. Retrieved from http://arxiv.org/abs/1909.02721

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Exosomes Extraction and Identification

Wu, X., Showiheen, S. A. A., Sun, A. R., Crawford, R., Xiao, Y., Mao, X., & Prasadam, I. (2019). Exosomes Extraction and Identification. https://doi.org/10.1007/978-1-4939-9769-5_4

View more

Metal ion levels post primary unilateral total knee arthroplasty

Masoumiganjgah, A., Ginsel, B., Whitehouse, S. L., Vijaysegaran, P., English, H., Crawford, R. W., & Crawford, R. (2019). Metal ion levels post primary unilateral total knee arthroplasty. AMJ, 12(7), 206–213. https://doi.org/10.21767/AMJ.2019.3599

View more

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Coory, J. A., Tan, K. G., Whitehouse, S. L., Hatton, A., Graves, S. E., & Crawford, R. W. (2019). The Outcome of Total Knee Arthroplasty With and Without Patellar Resurfacing up to 17 Years: A Report From the Australian Orthopaedic Association National Joint Replacement Registry. The Journal of Arthroplasty. https://doi.org/10.1016/j.arth.2019.08.007

View more

Robotic manipulation and the role of the task in the metric of success

Ortenzi, V., Controzzi, M., Cini, F., Leitner, J., Bianchi, M., Roa, M. A., & Corke, P. (2019). Robotic manipulation and the role of the task in the metric of success. Nature Machine Intelligence, 1(8), 340–346. https://doi.org/10.1038/s42256-019-0078-4

View more

A study of X-vector based speaker recognition on short utterances

Kanagasundaram, A., Sridharan, S., Ganapathy, S., Singh, P., & Fookes, C. B. (2019). A study of X-vector based speaker recognition on short utterances.

View more

Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning

Nash, W. T., Powell, C. J., Drummond, T., & Birbilis, N. (2019). Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning. Retrieved from http://arxiv.org/abs/1908.02548

View more

Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments

Zapotezny-Anderson, P., & Lehnert, C. (2019). Towards Active Robotic Vision in Agriculture: A Deep Learning Approach to Visual Servoing in Occluded and Unstructured Protected Cropping Environments. Retrieved from http://arxiv.org/abs/1908.01885

View more

Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention

Opazo, C. R., Marrese-Taylor, E., Saleh, F. S., Li, H., & Gould, S. (2019). Proposal-free Temporal Moment Localization of a Natural-Language Query in Video using Guided Attention. Retrieved from http://arxiv.org/abs/1908.07236

View more

Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization

Shi,Y., Liu, L., Yu, X., Li, H., Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization, accepted at NEUIPS 2019

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Question-Agnostic Attention for Visual Question Answering

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View more

Learning Variations in Human Motion via Mix-and-Match Perturbation

Aliakbarian, M. S., Saleh, F. S., Salzmann, M., Petersson, L., Gould, S., & Habibian, A. (2019). Learning Variations in Human Motion via Mix-and-Match Perturbation. Retrieved from http://arxiv.org/abs/1908.00733

View more

Cousin Network Guided Sketch Recognition via Latent Attribute Warehouse

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View more

Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects

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View more

V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

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View more

Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality

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View more

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

Bian, J.-W., Wu, Y.-H., Zhao, J., Liu, Y., Zhang, L., Cheng, M.-M., & Reid, I. (n.d.). An Evaluation of Feature Matchers for Fundamental Matrix Estimation. Retrieved from https://jwbian.net/Papers/FM_BMVC19.pdf

View more

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (n.d.). A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing. Retrieved from https://arxiv.org/pdf/1907.09642.pdf

View more

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Pang, G., Shen, C., & Van Den Hengel, A. (n.d.). Deep Anomaly Detection with Deviation Networks. https://doi.org/10.1145/3292500.3330871

View more

Model-free Tracker for Multiple Objects Using Joint Appearance and Motion Inference

Liu, C., Yao, R., Rezatofighi, S. H., Reid, I., & Shi, Q. (2019). Model-free tracker for multiple objects using joint appearance and motion inference. IEEE Transactions on Image Processing, 1–1. https://doi.org/10.1109/TIP.2019.2928123

View more

Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation

Neshat, M., Abbasnejad, E., Shi, Q., Alexander, B., & Wagner, M. (2019). ADAPTIVE NEURO-SURROGATE-BASED OPTIMISATION METHOD FOR WAVE ENERGY CONVERTERS PLACEMENT OPTIMISATION A PREPRINT. Retrieved from https://arxiv.org/pdf/1907.03076.pdf

View more

Unsupervised Task Design to Meta-Train Medical Image Classifiers

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View more

A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing

Liu, W., Zhang, P., Huang, X., Yang, J., Shen, C., & Reid, I. (n.d.). A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing. Retrieved from https://arxiv.org/pdf/1907.09642.pdf

View more

Real-Time Correlation Tracking via Joint Model Compression and Transfer

Wang, N., Zhou, W., Song, Y., Ma, C., & Li, H. (2019). Real-Time Correlation Tracking via Joint Model Compression and Transfer. Retrieved from https://arxiv.org/pdf/1907.09831

View more

Inverse Optimal Control for Multiphase Cost Functions

Herath, S., Fernando, B., & Harandi, M. (2019). Using temporal information for recognizing actions from still images. Pattern Recognition, 96, 106989. https://doi.org/10.1016/J.PATCOG.2019.106989

View more

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Herath, S., Fernando, B., & Harandi, M. (2019). Using temporal information for recognizing actions from still images. Pattern Recognition, 96, 106989. https://doi.org/10.1016/J.PATCOG.2019.106989

View more

Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors

Ferrarini, B., Waheed, M., Waheed, S., Ehsan, S., Milford, M., & McDonald-Maier, K. D. (2019). Visual Place Recognition for Aerial Robotics: Exploring Accuracy-Computation Trade-off for Local Image Descriptors. 2019 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 103–108. https://doi.org/10.1109/AHS.2019.00011

View more

EMPNet: Neural Localisation and Mapping Using Embedded Memory Points

Avraham, G., Zuo, Y., Dharmasiri, T., & Drummond, T. (2019). EMPNet: Neural Localisation and Mapping Using Embedded Memory Points. Retrieved from http://arxiv.org/abs/1907.13268

View more

Immunoregulatory role of exosomes derived from differentiating mesenchymal stromal cells on inflammation and osteogenesis

Wei, F., Li, Z., Crawford, R., Xiao, Y., & Zhou, Y. (2019). Immunoregulatory role of exosomes derived from differentiating mesenchymal stromal cells on inflammation and osteogenesis. Journal of Tissue Engineering and Regenerative Medicine, term.2947. https://doi.org/10.1002/term.2947

View more

Improving User Specifications for Robot Behavior through Active Preference Learning: Framework and Evaluation

Wilde, N., Blidaru, A., Smith, S. L., & Kulić, D. (2019). Improving User Specifications for Robot Behavior through Active Preference Learning: Framework and Evaluation. Retrieved from http://arxiv.org/abs/1907.10412

View more

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View more

Visual Controllers for Relative Positioning in Indoor Settings

Mejias, L., & Campoy, P. (2019). Visual controllers for relative positioning in indoor settings. Retrieved from https://eprints.qut.edu.au/131165/

View more

Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management

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View more

Optimal Feature Transport for Cross-View Image Geo-Localization

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View more

Densely Residual Laplacian Super-Resolution

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View more

Human Detection Aided by Deeply Learned Semantic Masks

"X. Wang, C. Shen, H. Li and S. Xu, ""Human Detection Aided by Deeply Learned Semantic Masks,"" in IEEE Transactions on Circuits and Systems for Video Technology. doi: 10.1109/TCSVT.2019.2924912"

View more

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Wang, Y., Shi, Q., Abbasnejad, E., Ma, C., Ma, X., & Zeng, B. (2019). Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes. Retrieved from https://arxiv.org/pdf/1906.09433

View more

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Parra, Á., Chin, T.-J., Eriksson, A., & Reid, I. (2019). Visual SLAM: Why Bundle Adjust? Retrieved from https://cs.adelaide.edu.au/~aparra/papers/parra19_icra_poster

View more

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View more

CVPR19 Tracking and Detection Challenge: How crowded can it get?

Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., … Taixé, T. (n.d.). CVPR19 Tracking and Detection Challenge: How crowded can it get? Retrieved from http://www.motchallenge.net/

View more

SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks

Abedin, A., Rezatofighi, S. H., Shi, Q., & Ranasinghe, D. C. (2019). SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks. Retrieved from https://arxiv.org/pdf/1906.02399

View more

Event-based Star Tracking via Multiresolution Progressive Hough Transforms

Chin, T.-J., & Bagchi, S. (2019). Event-based Star Tracking via Multiresolution Progressive Hough Transforms. Retrieved from https://arxiv.org/pdf/1906.07866

View more

BTEL: A Binary Tree Encoding Approach for Visual Localization

Le, H., Hoang, T., & Milford, M. (2019). BTEL: A Binary Tree Encoding Approach for Visual Localization. Retrieved from https://arxiv.org/abs/1906.11992

View more

Filter Early, Match Late: Improving Network-Based Visual Place Recognition

Hausler, S., Jacobson, A., & Milford, M. (2019). Filter Early, Match Late: Improving Network-Based Visual Place Recognition. Retrieved from https://arxiv.org/abs/1906.12176

View more

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Morrison, D., Corke, P., & Leitner, J. (2019). Learning robust, real-time, reactive robotic grasping. The International Journal of Robotics Research, 027836491985906. https://doi.org/10.1177/0278364919859066

View more

A decade of Australian and New Zealand orthopaedic publications: a bibliometric trend analysis from 2008 to 2018

Churchill, A. W., Malacova, E., Journeaux, S. F., Richardson, M., Crawford, R., & Vickers, M. L. (2019). A decade of Australian and New Zealand orthopaedic publications: a bibliometric trend analysis from 2008 to 2018. International Orthopaedics, 1–10. https://doi.org/10.1007/s00264-019-04359-1

View more

Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control

Molloy, T. L., Inga, J., Flad, M., Ford, J. J., Perez, T., & Hohmann, S. (n.d.). Inverse Open-Loop Noncooperative Differential Games and Inverse Optimal Control. https://doi.org/10.1109/TAC.2019.2921835

View more

Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators

Hojnik, T., Flick, P., Bandyopadhyay, T., & Roberts, J. (n.d.). Dynamic Manipulation of Gear Ratio and Ride Height for a Novel Compliant Wheel using Pneumatic Actuators. Retrieved from https://eprints.qut.edu.au/130717/

View more

Dense Deformation Network for High Resolution Tissue Cleared Image Registration

Nazib, A., Fookes, C., & Perrin, D. (2019). Dense Deformation Network for High Resolution Tissue Cleared Image Registration. Retrieved from https://arxiv.org/pdf/1906.06180

View more

Embracing Contact: Pushing Multiple Objects with Robot’s Forearm

Cosgun, A., Ditria, L., D’Lima, S., & Drummond, T. (2019). Embracing Contact: Pushing Multiple Objects with Robot’s Forearm. Retrieved from http://arxiv.org/abs/1906.06866

View more

Benchmarking Sampling-based Probabilistic Object Detectors

Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (n.d.). Benchmarking Sampling-based Probabilistic Object Detectors. Retrieved from http://openaccess.thecvf.com/content_CVPRW_2019/papers/Uncertainty and Robustness in Deep Visual Learning/Miller_Benchmarking_Sampling-based_Probabilistic_Object_Detectors_CVPRW_2019_paper.pdf

View more

Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose

Cerón, A., Prieto, F., & Mejias, L. (2019). Vision-Based Path Finding Strategy of Unmanned Aerial Vehicles for Electrical Infrastructure Purpose. In Path Planning for Autonomous Vehicles [Working Title]. https://doi.org/10.5772/intechopen.86689

View more

Parallel Optimal Transport GAN

Avraham, G., Zuo, Y., & Drummond, T. (n.d.). Parallel Optimal Transport GAN *. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/papers/Avraham_Parallel_Optimal_Transport_GAN_CVPR_2019_paper.pdf

View more

Event Cameras, Contrast Maximization and Reward Functions: An Analysis

Stoffregen, T., & Kleeman, L. (n.d.). Event Cameras, Contrast Maximization and Reward Functions: an Analysis. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/papers/Stoffregen_Event_Cameras_Contrast_Maximization_and_Reward_Functions_An_Analysis_CVPR_2019_paper.pdf

View more

Caricaturing can improve facial expression recognition in low-resolution images and age-related macular degeneration

https://scholar.google.com.au/citations?hl=en&user=yMXs1WcAAAAJ&view_op=list_works&sortby=pubdate#d=gs_md_cita-d&u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Den%26user%3DyMXs1WcAAAAJ%26sortby%3Dpubdate%26citation_for_view%3DyMXs1WcAAAAJ%3AIUKN3-7HHlwC%26tzom%3D-600

View more

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Lee, N., Ajanthan, T., Gould, S., & Torr, P. H. S. (2019). A Signal Propagation Perspective for Pruning Neural Networks at Initialization. Retrieved from http://arxiv.org/abs/1906.06307

View more

Event-based Star Tracking via Multiresolution Progressive Hough Transforms

Chin, T.-J., & Bagchi, S. (2019). Event-based Star Tracking via Multiresolution Progressive Hough Transforms. Retrieved from https://arxiv.org/pdf/1906.07866

View more

One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization

Nascimento, J. C., & Carneiro, G. (n.d.). ONE SHOT SEGMENTATION: UNIFYING RIGID DETECTION AND NON-RIGID SEGMENTATION USING ELASTIC REGULARIZATION 1 One shot segmentation: unifying rigid detection and non-rigid segmentation using elastic regularization. Retrieved from https://cs.adelaide.edu.au/~carneiro/publications/PAMI-TPS-LAST-VERSION.pdf

View more

Towards End-to-End Text Spotting in Natural Scenes

Li, H., Wang, P., & Shen, C. (2019). Towards End-to-End Text Spotting in Natural Scenes. Retrieved from https://arxiv.org/pdf/1906.06013.pdf

View more

Deep domain adaptation for anti-spoofing in speaker verification systems

Himawan, I., Villavicencio, F., Sridharan, S., & Fookes, C. (2019). Deep domain adaptation for anti-spoofing in speaker verification systems. Computer Speech & Language. https://doi.org/10.1016/J.CSL.2019.05.007

View more

Practical optimal registration of terrestrial LiDAR scan pairs

Cai, Z., Chin, T.-J., Bustos, A. P., & Schindler, K. (2019). Practical optimal registration of terrestrial LiDAR scan pairs. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 118–131. https://doi.org/10.1016/j.isprsjprs.2018.11.016

View more

Multi-modal Ensemble Classification for Generalized Zero Shot Learning

Felix, R., Sasdelli, M., Reid, I., & Carneiro, G. (2019). Multi-modal Ensemble Classification for Generalized Zero Shot Learning. Retrieved from https://arxiv.org/pdf/1901.04623

View more

Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes

Liu, Y., Liu, L., Rezatofighi, H., Do, T.-T., Shi, Q., & Reid, I. (2019). Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes. Retrieved from https://arxiv.org/pdf/1901.03796

View more

Accelerated Guided Sampling for Multistructure Model Fitting

"T. Lai, H. Wang, Y. Yan, T. Chin, J. Zheng and B. Li, ""Accelerated Guided Sampling for Multistructure Model Fitting,"" in IEEE Transactions on Cybernetics. doi: 10.1109/TCYB.2018.2889908"

View more

RefineNet: Multi-Path Refinement Networks for Dense Prediction

"G. Lin, F. Liu, A. Milan, C. Shen and I. Reid, ""RefineNet: Multi-Path Refinement Networks for Dense Prediction,"" in IEEE Transactions on Pattern Analysis and Machine Intelligence. doi: 10.1109/TPAMI.2019.2893630"

View more

Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss

"P. Zhang, W. Liu, H. Lu and C. Shen, ""Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss,"" in IEEE Transactions on Image Processing. doi: 10.1109/TIP.2019.2893535"

View more

Attention Residual Learning for Skin Lesion Classification

"J. Zhang, Y. Xie, Y. Xia and C. Shen, ""Attention Residual Learning for Skin Lesion Classification,"" in IEEE Transactions on Medical Imaging. doi: 10.1109/TMI.2019.2893944"

View more

Cardiovascular Diseases

Verjans J., Veldhuis W.B., Carneiro G., Wolterink J.M., Išgum I., Leiner T. (2019) Cardiovascular Diseases. In: Ranschaert E., Morozov S., Algra P. (eds) Artificial Intelligence in Medical Imaging. Springer, Cham

View more

A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints

Constraints´alvaro, C., Bustos, P., Chin, T.-J., Neumann, F., Friedrich, T., & Katzmann, M. (2019). A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints´Alvaro. Retrieved from https://arxiv.org/pdf/1902.01534.pdf

View more

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Retrieved from https://arxiv.org/pdf/1902.09630

View more

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

Li, J., Liu, Y., Gong, D., Shi, Q., Yuan, X., Zhao, C., & Reid, I. (2019). RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion. Retrieved from https://arxiv.org/pdf/1903.00620

View more

Associatively Segmenting Instances and Semantics in Point Clouds

Wang, X., Liu, S., Shen, X., Shen, C., & Jia, J. (2019). Associatively Segmenting Instances and Semantics in Point Clouds. Retrieved from https://github.com/WXinlong/ASIS.

View more

Self-supervised Learning for Single View Depth and Surface Normal Estimation

Zhan, H., Saroj Weerasekera, C., Garg, R., & Reid, I. (2019). Self-supervised Learning for Single View Depth and Surface Normal Estimation. Retrieved from https://arxiv.org/pdf/1903.00112

View more

Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation

"T. Do et al., ""Binary Constrained Deep Hashing Network for Image Retrieval Without Manual Annotation,"" 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 695-704. doi: 10.1109/WACV.2019.00079"

View more

Multi-Scale Dense Networks for Deep High Dynamic Range Imaging

"Q. Yan et al., ""Multi-Scale Dense Networks for Deep High Dynamic Range Imaging,"" 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 2019, pp. 41-50. doi: 10.1109/WACV.2019.00012"

View more

CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

Zhang, C., Lin, G., Liu, F., Yao, R., & Shen, C. (2019). CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning. Retrieved from https://arxiv.org/pdf/1903.02351

View more

Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector

Hunter J., Jateff E., van den Hengel A. (2019) Using Digital Visualization of Archival Sources to Enhance Archaeological Interpretation of the ‘Life History’ of Ships: The Case Study of HMCS/HMAS Protector. In: McCarthy J., Benjamin J., Winton T., van Duivenvoorde W. (eds) 3D Recording and Interpretation for Maritime Archaeology. Coastal Research Library, vol 31. Springer, Cham

View more

Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation

Tian, Z., He, T., Shen, C., & Yan, Y. (2019). Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation *. Retrieved from https://arxiv.org/pdf/1903.02120

View more

Knowledge Adaptation for Efficient Semantic Segmentation

He, T., Shen, C., Tian, Z., Gong, D., Sun, C., & Yan, Y. (2019). Knowledge Adaptation for Efficient Semantic Segmentation *. Retrieved from https://arxiv.org/pdf/1903.04688

View more

Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2019). Semi-and Weakly Supervised Directional Bootstrapping Model for Automated Skin Lesion Segmentation. Retrieved from https://arxiv.org/pdf/1903.03313

View more

Learning Distilled Graph for Large-scale Social Network Data Clusterin

W. Liu, D. Gong, M. Tan, Q. Shi, Y. Yang and A. G. Hauptmann, ""Learning Distilled Graph for Large-scale Social Network Data Clustering,"" in IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2019.2904068

View more

Robust Foreground Segmentationand Image Registration for Optical Detection of GEO Objects

Do, H.N., Chin, T-J., Moretti, N., Jah, M.K., Tetlow, M., Robust Foreground Segmentationand Image Registration for Optical Detection of GEO Objects, Advances in Space Research (2019), doi: https://doi.org/10.1016/j.asr.2019.03.008

View more

Accurate Imagery Recovery Using a Multi-Observation Patch Model

Lei Zhang, Wei Wei, Qinfeng Shi, Chunhua Shen, Anton van den Hengel, Yanning Zhang, Accurate imagery recovery using a multi-observation patch model, Information Sciences, 2019, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2019.03.033.

View more

Training Quantized Network with Auxiliary Gradient Module

Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2019). Training Quantized Network with Auxiliary Gradient Module. Retrieved from https://arxiv.org/pdf/1903.11236.pdf

View more

Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

Guo, Y., Chen, Q., Chen, J., Wu, Q., Shi, Q., & Tan, M. (2019). Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis. Retrieved from https://arxiv.org/pdf/1903.11250

View more

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection

Gong, D., Liu, L., Le, V., Saha, B., Mansour, R., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection. Retrieved from https://donggong1.github.io/anomdec-memae

View more

Actively Seeking and Learning from Live Data

Teney, D., & Van Den Hengel, A. (2019). Actively Seeking and Learning from Live Data. Retrieved from https://arxiv.org/pdf/1904.02865

View more

Reinforcement Learning with Attention that Works: A Self-Supervised Approach

Manchin, A., Abbasnejad, E., & Van Den Hengel, A. (2019). Reinforcement Learning with Attention that Works: A Self-Supervised Approach. Retrieved from https://arxiv.org/pdf/1904.03367

View more

Architecture Search of Dynamic Cells for Semantic Video Segmentation

Nekrasov, V., Chen, H., Shen, C., & Reid, I. (2019). Architecture Search of Dynamic Cells for Semantic Video Segmentation. Retrieved from https://arxiv.org/pdf/1904.02371

View more

FCOS: Fully Convolutional One-Stage Object Detection

Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection. Retrieved from https://arxiv.org/pdf/1904.01355

View more

Template-Based Automatic Search of Compact Semantic Segmentation Architectures

Nekrasov, V., Shen, C., & Reid, I. (2019). Template-Based Automatic Search of Compact Semantic Segmentation Architectures. Retrieved from https://arxiv.org/pdf/1904.02365

View more

A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning

Do, T.-T., Tran, T., Reid, I., Kumar, V., Hoang, T., & Carneiro, G. (2019). A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning. Retrieved from https://arxiv.org/pdf/1904.08720

View more

Attention-guided Network for Ghost-free High Dynamic Range Imaging

Yan, Q., Gong, D., Shi, Q., van den Hengel, A., Shen, C., Reid, I., & Zhang, Y. (2019). Attention-guided Network for Ghost-free High Dynamic Range Imaging. Retrieved from https://donggong1.github.io/ahdr

View more

V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation

Nguyen, A., Do, T.-T., Reid, I., Caldwell, D. G., & Tsagarakis, N. G. (2019). V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation. Retrieved from http://arxiv.org/abs/1903.10869

View more

Bayesian Generative Active Deep Learning

Tran, T., Do, T.-T., Reid, I., & Carneiro, G. (2019). Bayesian Generative Active Deep Learning. Retrieved from https://arxiv.org/pdf/1904.11643

View more

Heritage image annotation via collective knowledge

Zhang, J., Wu, Q., Zhang, J., Shen, C., Lu, J., & Wu, Q. (2019). Heritage image annotation via collective knowledge. Pattern Recognition. https://doi.org/10.1016/j.patcog.2019.04.017

View more

An Effective Two-Branch Model-Based Deep Network for Single Image Deraining

Wang, Y., Gong, D., Yang, J., Shi, Q., Hengel, A. van den, Xie, D., & Zeng, B. (2019). An Effective Two-Branch Model-Based Deep Network for Single Image Deraining. Retrieved from http://arxiv.org/abs/1905.05404

View more

TopNet: Structural Point Cloud Decoder

Tchapmi, L. P., Kosaraju, V., Rezatofighi, H., Reid, I., & Savarese, S. (2019). TopNet: Structural Point Cloud Decoder. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Tchapmi_TopNet_Structural_Point_Cloud_Decoder_CVPR_2019_paper.html

View more

A Generative Adversarial Density Estimator

Ehsan Abbasnejad, M., Shi, Q., van den Hengel, A., & Liu, L. (2019). A Generative Adversarial Density Estimator. Retrieved from http://openaccess.thecvf.com/content_CVPR_2019/html/Abbasnejad_A_Generative_Adversarial_Density_Estimator_CVPR_2019_paper.html

View more

Event-Based Motion Segmentation by Motion Compensation

Stoffregen, T., Gallego, G., Drummond, T., Kleeman, L., & Scaramuzza, D. (2019). Event-Based Motion Segmentation by Motion Compensation. Retrieved from http://arxiv.org/abs/1904.01293

View more

CED: Color Event Camera Dataset

Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., & Scaramuzza, D. (2019). CED: Color Event Camera Dataset. Retrieved from https://arxiv.org/pdf/1904.10772

View more

Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks

Huang, W., Fan, L., Harandi, M., Ma, L., Liu, H., Liu, W., & Gan, C. (2019). Toward Efficient Action Recognition: Principal Backpropagation for Training Two-Stream Networks. IEEE Transactions on Image Processing, 28(4), 1773–1782. https://doi.org/10.1109/TIP.2018.2877936

View more

Min-Max Statistical Alignment for Transfer Learning

Herath, S., Harandi, M., Fernando, B., Nock, R., & Basura, F. (n.d.). Min-Max Statistical Alignment for Transfer Learning. Retrieved from http://users.cecs.anu.edu.au/~rnock/docs/cvpr19-hhfn-camera-ready-main.pdf

View more

Online near time-optimal trajectory planning for industrial robots

Kim, J., & Croft, E. A. (2019). Online near time-optimal trajectory planning for industrial robots. Robotics and Computer-Integrated Manufacturing, 58, 158–171. https://doi.org/10.1016/J.RCIM.2019.02.009

View more

Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM

Hall, W., Schmader, T., Aday, A., & Croft, E. (n.d.). Decoding the Dynamics of Social Identity Threat in the Workplace: A Within-Person Analysis of Women’s and Men’s Interactions in STEM. https://doi.org/10.1177/1948550618772582

View more

Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations

Radmard, S., Moon, Aj., & Croft, E. A. (2019). Impacts of Visual Occlusion and Its Resolution in Robot-Mediated Social Collaborations. International Journal of Social Robotics, 11(1), 105–121. https://doi.org/10.1007/s12369-018-0480-9

View more

An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers

Chan, W. P., Pan, M. K. X. J., Croft, E. A., & Inaba, M. (2019). An Affordance and Distance Minimization Based Method for Computing Object Orientations for Robot Human Handovers. International Journal of Social Robotics, 1–20. https://doi.org/10.1007/s12369-019-00546-7

View more

Stable Gaussian process based tracking control of Euler–Lagrange systems

Beckers, T., Kulić, D., & Hirche, S. (2019). Stable Gaussian process based tracking control of Euler–Lagrange systems. Automatica, 103, 390–397. https://doi.org/10.1016/J.AUTOMATICA.2019.01.023

View more

Expression of Curiosity in Social Robots

Ceha, J., Chhibber, N., Goh, J., McDonald, C., Oudeyer, P.-Y., Kulić, D., & Law, E. (2019). Expression of Curiosity in Social Robots. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems - CHI ’19, 1–12. https://doi.org/10.1145/3290605.3300636

View more

Learning to Engage with Interactive Systems: A Field Study

Meng, L., Lin, D., Francey, A., Gorbet, R., Beesley, P., & Kuli´c, D. K. (2019). LEARNING TO ENGAGE WITH INTERACTIVE SYSTEMS: A FIELD STUDY A PREPRINT. Retrieved from http://www.philipbeesleyarchitect.com/sculptures/

View more

Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions

Wilde, N., Kulic, D., & Smith, S. L. (2019). Bayesian Active Learning for Collaborative Task Specification Using Equivalence Regions. IEEE Robotics and Automation Letters, 4(2), 1691–1698. https://doi.org/10.1109/LRA.2019.2897342

View more

The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

Meyer, B. J., & Drummond, T. (2019). The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning. Retrieved from https://arxiv.org/pdf/1902.10363

View more

Learning to Take Good Pictures of People with a Robot Photographer

Newbury, R., Cosgun, A., Koseoglu, M., & Drummond, T. (2019). Learning to Take Good Pictures of People with a Robot Photographer. Retrieved from https://arxiv.org/pdf/1904.05688

View more

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Le, H., Hoang, T., Zhang, Q., Do, T.-T., Eriksson, A., & Milford, M. (2019). SASSE: Scalable and Adaptable 6-DOF Pose Estimation. Retrieved from https://arxiv.org/pdf/1902.01549

View more

Visual SLAM: Why Bundle Adjust?

Parra Bustos, A., Chin, T.-J., Eriksson, A., & Reid, I. (2019). Visual SLAM: Why Bundle Adjust? Retrieved from https://arxiv.org/pdf/1902.03747

View more

RERERE: Remote Embodied Referring Expressions in Real indoor Environments

Qi, Y., Wu, Q., Anderson, P., Liu, M., Shen, C., & Van Den Hengel, A. (2019). RERERE: Remote Embodied Referring Expressions in Real indoor Environments. Retrieved from https://arxiv.org/pdf/1904.10151

View more

Constrained Design of Deep Iris Networks

Nguyen, K., Fookes, C., & Sridharan, S. (2019). Constrained Design of Deep Iris Networks. Retrieved from https://arxiv.org/pdf/1905.09481

View more

Homography estimation of a moving planar scene from direct point correspondence

De Marco, S., Hua, M. D., Mahony, R., & Hamel, T. (2019). Homography estimation of a moving planar scene from direct point correspondence. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2018–Decem, pp. 565–570). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CDC.2018.8619386

View more

Learning to Adapt for Stereo

Tonioni, A., Rahnama, O., Joy, T., Di Stefano, L., Ajanthan, T., & Torr, P. H. S. (2019). Learning to Adapt for Stereo. Retrieved from http://arxiv.org/abs/1904.02957

View more

A Deep Journey into Super-resolution: A survey

Anwar, S., Khan, S., & Barnes, N. (2019). A Deep Journey into Super-resolution: A survey. Retrieved from http://arxiv.org/abs/1904.07523

View more

Super-Trajectories: A Compact Yet Rich Video Representation

Akhter, I., Fah, C. L., & Hartley, R. (2019). Super-Trajectories: A Compact Yet Rich Video Representation. Retrieved from http://arxiv.org/abs/1901.07273

View more

Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment

Zhou, Y., Li, H., & Kneip, L. (2019). Canny-VO: Visual Odometry with RGB-D Cameras Based on Geometric 3-D-2-D Edge Alignment. In IEEE Transactions on Robotics (Vol. 35, pp. 184–199). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/TRO.2018.2875382

View more

Adversarial spatio-temporal learning for video deblurring

Zhang, K., Luo, W., Zhong, Y., Ma, L., Liu, W., & Li, H. (2019). Adversarial spatio-temporal learning for video deblurring. IEEE Transactions on Image Processing, 28(1), 291–301. https://doi.org/10.1109/TIP.2018.2867733

View more

Bringing Blurry Alive at High Frame-Rate with an Event Camera

Pan, L., Hartley, R., Scheerlinck, C., Liu, M., Yu, X., & Dai, Y. (2019). Bringing Blurry Alive at High Frame-Rate with an Event Camera. Retrieved from http://arxiv.org/abs/1903.06531

View more

Single image deblurring and camera motion estimation with depth map

Pan, L., Dai, Y., & Liu, M. (2019). Single image deblurring and camera motion estimation with depth map. In Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 (pp. 2116–2125). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/WACV.2019.00229

View more

Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation

Kumar, S., Ghorakavi, R. S., Dai, Y., & Li, H. (2019). Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation. Retrieved from http://arxiv.org/abs/1902.03791

View more

Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry

Zhou, D., Dai, Y., & Li, H. (2019). Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry. Retrieved from http://arxiv.org/abs/1903.00912

View more

Continual Learning with Tiny Episodic Memories

Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P. K., Torr, P. H. S., & Ranzato, M. (2019). Continual Learning with Tiny Episodic Memories. Retrieved from http://arxiv.org/abs/1902.10486

View more

On the structure of kinematic systems with complete symmetry

Trumpf, J., Mahony, R., & Hamel, T. (2019). On the structure of kinematic systems with complete symmetry. In Proceedings of the IEEE Conference on Decision and Control (Vol. 2018–Decem, pp. 1276–1280). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CDC.2018.8619718

View more

Neural Collaborative Subspace Clustering

Zhang, T., Ji, P., Harandi, M., Huang, W., & Li, H. (2019). Neural Collaborative Subspace Clustering. Retrieved from http://arxiv.org/abs/1904.10596

View more

Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images

Zhu, H., Guo, M., Li, H., Wang, Q., & Robles-Kelly, A. (2019). Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images. Retrieved from http://arxiv.org/abs/1902.05672

View more

Practical Robot Learning from Demonstrations using Deep End-to-End Training

Cosgun, A., Rowntree, T., Reid, I., & Drummond, T. (2019). Practical Robot Learning from Demonstrations using Deep End-to-End Training. Retrieved from https://arxiv.org/pdf/1905.09025

View more

Deep Learning AI for Corrosion Detection

Nash, W., Drummond, T., & Birbilis, N. (2019, May 15). Deep Learning AI for Corrosion Detection. Retrieved from https://www.onepetro.org/conference-paper/NACE-2019-13267

View more

Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach

Showiheen, S. A. A., Sun, A. R., Wu, X., Crawford, R., Xiao, Y., Wellard, R. M., & Prasadam, I. (2019). Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach. Current Rheumatology Reports, 21(6), 26. https://doi.org/10.1007/s11926-019-0827-8

View more

Picking the right robotics challenge

Leitner, J. (2019). Picking the right robotics challenge. Nature Machine Intelligence, 1(3), 162–162. https://doi.org/10.1038/s42256-019-0031-6

View more

Quickest Detection and Identification of Intermittent Signals with Application to Vision Based Aircraft Detection

James, J., Ford, J. J., & Molloy, T. L. (2019). Quickest Detection and Identification of Intermittent Signals with Application to Vision Based Aircraft Detection. Retrieved from http://arxiv.org/abs/1903.03270

View more

On the Informativeness of Measurements in Shiryaev’s Bayesian Quickest Change Detection

Ford, J. J., James, J., & Molloy, T. L. (2019). On the Informativeness of Measurements in Shiryaev’s Bayesian Quickest Change Detection. Retrieved from http://arxiv.org/abs/1903.03283

View more

Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid

James, J., Ford, J. J., & Molloy, T. L. (2019). Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid. Retrieved from http://arxiv.org/abs/1903.03275

View more

Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization

Wu, L., Crawford, R., & Roberts, J. (2019). Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization. Retrieved from http://arxiv.org/abs/1902.00198

View more

Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy

Marmol, A., Banach, A., & Peynot, T. (2019). Dense-ArthroSLAM: dense intra-articular 3D reconstruction with robust localization prior for arthroscopy. IEEE Robotics and Automation Letters, 4(2), 918–925. https://doi.org/10.1109/LRA.2019.2892199

View more

Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm

Razjigaev, A., Pandey, A. K., Roberts, J., & Wu, L. (2019). Optimal Dexterity for a Snake-like Surgical Manipulator using Patient-specific Task-space Constraints in a Computational Design Algorithm. Retrieved from http://arxiv.org/abs/1903.02217

View more

Modular field robot deployment for inspection of dilapidated buildings

Cordie, T. P., Bandyopadhyay, T., Roberts, J., Dunbabin, M., Greenop, K., Dungavell, R., & Steindl, R. (2019). Modular field robot deployment for inspection of dilapidated buildings. Journal of Field Robotics, rob.21872. https://doi.org/10.1002/rob.21872

View more

Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2019). Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion. IEEE Robotics and Automation Letters, 4(2), 177–184. https://doi.org/10.1109/LRA.2018.2884765

View more

On the choice of grasp type and location when handing over an object

Cini, F., Ortenzi, V., Corke, P., & Controzzi, M. (2019). On the choice of grasp type and location when handing over an object. Science Robotics, 4(27), eaau9757. https://doi.org/10.1126/scirobotics.aau9757

View more

Learning to Fuse Multiscale Features for Visual Place Recognition

Mao, J., Hu, X., He, X., Zhang, L., Wu, L., & Milford, M. J. (2019). Learning to Fuse Multiscale Features for Visual Place Recognition. IEEE Access, 7, 5723–5735. https://doi.org/10.1109/ACCESS.2018.2889030

View more

SASSE: Scalable and Adaptable 6-DOF Pose Estimation

Le, H., Hoang, T., Zhang, Q., Do, T.-T., Eriksson, A., & Milford, M. (2019). SASSE: Scalable and Adaptable 6-DOF Pose Estimation. Retrieved from https://arxiv.org/abs/1902.01549

View more

LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles

Zeng, F., Jacobson, A., Smith, D., Boswell, N., Peynot, T., & Milford, M. (2019). LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles. Retrieved from https://arxiv.org/abs/1903.08313

View more

Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., & McDonald-Maier, K. (2019). Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions. Retrieved from https://arxiv.org/abs/1903.09107

View more

Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods

Hausler, S., Jacobson, A., & Milford, M. (2019). Multi-Process Fusion: Visual Place Recognition Using Multiple Image Processing Methods. IEEE Robotics and Automation Letters, 4(2), 1924–1931. https://doi.org/10.1109/LRA.2019.2898427

View more

Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Zaffar, M., Khaliq, A., Ehsan, S., Milford, M., Alexis, K., & McDonald-Maier, K. (2019). Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics? Retrieved from http://arxiv.org/abs/1904.07967

View more

Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors

Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. Retrieved from http://arxiv.org/abs/1903.06391

View more

The Probabilistic Object Detection Challenge

Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge. Retrieved from http://arxiv.org/abs/1903.07840

View more

Semantic–geometric visual place recognition: a new perspective for reconciling opposing views

Garg, S., Suenderhauf, N., & Milford, M. (2019). Semantic–geometric visual place recognition: a new perspective for reconciling opposing views. The International Journal of Robotics Research, 027836491983976. https://doi.org/10.1177/0278364919839761

View more

Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

Garg, S., Babu, M., Dharmasiri, T., Hausler, S., Suenderhauf, N., Kumar, S., Drummond, T., & Milford, M. (2019). Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation. Retrieved from http://arxiv.org/abs/1902.07381

View more

Model-less Active Compliance for Continuum Robots using Recurrent Neural Networks

Jakes, D., Ge, Z., & Wu, L. (2019). Model-less Active Compliance for Continuum Robots using Recurrent Neural Networks. Retrieved from http://arxiv.org/abs/1902.08943

View more

Feature-based recursive observer design for homography estimation and its application to image stabilization

Hua, M.-D., Trumpf, J., Hamel, T., Mahony, R., & Morin, P. (2019). Feature-based recursive observer design for homography estimation and its application to image stabilization. Asian Journal of Control. https://doi.org/10.1002/asjc.2012

View more

An Adaptive Markov Random Field for Structured Compressive Sensing

Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D. C., & Qinfeng Shi, J. (2019). An Adaptive Markov Random Field for Structured Compressive Sensing. IEEE Transactions on Image Processing, 28(3), 1556–1570. http://doi.org/10.1109/TIP.2018.2878294

View more

One-step adaptive markov random field for structured compressive sensing

Suwichaya Suwanwimolkul, Lei Zhang, Damith C. Ranasinghe, Qinfeng Shi, One-step adaptive markov random field for structured compressive sensing, Signal Processing, Volume 156, 2019,Pages 116-144, ISSN 0165-1684, https://doi.org/10.1016/j.sigpro.2018.10.020.

View more

Recovering Faces From Portraits with Auxiliary Facial Attributes

*Shiri, F., Yu, X., Porikli, F., Hartley, R., & Koniusz, P. (2019). Recovering Faces From Portraits with Auxiliary Facial Attributes. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 406–415). Waikoloa Village, Hawaii, United States: IEEE. http://doi.org/10.1109/WACV.2019.00049

View more

On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC

Tran, N.-T., Le Tan, D.-K., Doan, A.-D., Do, T.-T., Bui, T.-A., Tan, M., & Cheung, N.-M. (2019). On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC. IEEE Transactions on Image Processing, 28(4), 1675–1690. http://doi.org/10.1109/TIP.2018.2881829

View more

QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM

Nicholson, L., Milford, M., & Sunderhauf, N. (2019). QuadricSLAM: Dual Quadrics From Object Detections as Landmarks in Object-Oriented SLAM. IEEE Robotics and Automation Letters, 4(1), 1–8. http://doi.org/10.1109/LRA.2018.2866205

View more

Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion

Tsai, D., Dansereau, D. G., Peynot, T., & Corke, P. (2019). Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion. IEEE Robotics and Automation Letters, 4(2), 177–184. http://doi.org/10.1109/LRA.2018.2884765

View more

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