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Publications

2019 Submitted [125]

MTRNet: A Generic Scene Text Eraser

Tursun, O., Zeng, R., Denman, S., Sivapalan, S., Sridharan, S., & Fookes, C. (2019). MTRNet: A Generic Scene Text Eraser. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 39–44. https://doi.org/10.1109/ICDAR.2019.00016

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On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints

Teney, D., Abbasnejad, E., & Hengel, A. van den. (2019). On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints. Retrieved from http://arxiv.org/abs/1909.13471

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Deep Weakly-supervised Anomaly Detection

Pang, G., Shen, C., Jin, H., & Hengel, A. van den. (2019). Deep Weakly-supervised Anomaly Detection. Retrieved from http://arxiv.org/abs/1910.13601

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Index Network

Lu, H., Dai, Y., Shen, C., & Xu, S. (2019). Index Network. Retrieved from http://arxiv.org/abs/1908.09895

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Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation

Shen, T., Gong, D., Zhang, W., Shen, C., & Mei, T. (2019). Regularizing Proxies with Multi-Adversarial Training for Unsupervised Domain-Adaptive Semantic Segmentation. Retrieved from http://arxiv.org/abs/1907.12282

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NAS-FCOS: Fast Neural Architecture Search for Object Detection

Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., & Zhang, Y. (2019). NAS-FCOS: Fast Neural Architecture Search for Object Detection. Retrieved from http://arxiv.org/abs/1906.04423

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A Simple and Strong Convolutional-Attention Network for Irregular Text Recognition

Yang, L., Wang, P., Li, H., Gao, Y., Zhang, L., Shen, C., & Zhang, Y. (2019). A Simple and Strong Convolutional-Attention Network for Irregular Text Recognition. Retrieved from http://arxiv.org/abs/1904.01375

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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Xie, Y., Zhang, J., Xia, Y., & Shen, C. (2019). A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification. IEEE Transactions on Medical Imaging, 1–1. Retrieved from http://arxiv.org/abs/1903.03313

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In defense of OSVOS

Liu, Y., Dai, Y., Doan, A.-D., Liu, L., & Reid, I. (2019). In defense of OSVOS. Retrieved from http://arxiv.org/abs/1908.06692

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Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation

Liu, J., Zhao, P., Liu, Y., Sheng, V. S., Zhuang, F., Xu, J., Zhou, X., & Xiong, H. (2019). Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation. Retrieved from http://arxiv.org/abs/1905.13030

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Deep Multiphase Level Set for Scene Parsing

Zhang, P., Liu, W., Lei, Y., Wang, H., & Lu, H. (2019). Deep Multiphase Level Set for Scene Parsing. Retrieved from http://arxiv.org/abs/1910.03166

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Uncertainty in Model-Agnostic Meta-Learning using Variational Inference

Nguyen, C., Do, T.-T., & Carneiro, G. (2019). Uncertainty in Model-Agnostic Meta-Learning using Variational Inference. Retrieved from http://arxiv.org/abs/1907.11864

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Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection

Liu, Y., He, T., Chen, H., Wang, X., Luo, C., Zhang, S., Shen, C, & Jin, L. (2019). Exploring the Capacity of Sequential-free Box Discretization Network for Omnidirectional Scene Text Detection. Retrieved from https://tinyurl.com/sbdnet

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NeuRoRA: Neural Robust Rotation Averaging

Purkait, P., Chin, T.-J., & Reid, I. (2019). NeuRoRA: Neural Robust Rotation Averaging. Retrieved from http://arxiv.org/abs/1912.04485

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Learning to generate new indoor scenes

Purkait, P., Zach, C., & Reid, I. (2019). Learning to generate new indoor scenes. Retrieved from http://arxiv.org/abs/1912.04554

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DirectPose: Direct End-to-End Multi-Person Pose Estimation

Tian, Z., Chen, H., & Shen, C. (2019). DirectPose: Direct End-to-End Multi-Person Pose Estimation. Retrieved from http://arxiv.org/abs/1911.07451

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Unified Multifaceted Feature Learning for Person Re-Identification

Yan, C., Pang, G., Bai, X., & Shen, C. (2019). Unified Multifaceted Feature Learning for Person Re-Identification. Retrieved from http://arxiv.org/abs/1911.08651

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To Balance or Not to Balance: An Embarrassingly Simple Approach for Learning with Long-Tailed Distributions

Zhang, J., Liu, L., Wang, P., & Shen, C. (2019). To Balance or Not to Balance: An Embarrassingly Simple Approach for Learning with Long-Tailed Distributions. Retrieved from http://arxiv.org/abs/1912.04486

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SOLO: Segmenting Objects by Locations

Wang, X., Kong, T., Shen, C., Jiang, Y., & Li, L. (2019). SOLO: Segmenting Objects by Locations. Retrieved from http://arxiv.org/abs/1912.04488

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Ordered or Orderless: A Revisit for Video based Person Re-Identification

Zhang, L., Shi, Z., Zhou, J. T., Cheng, M.-M., Liu, Y., Bian, J.-W., Zeng, Z. & Shen, C. (2019). Ordered or Orderless: A Revisit for Video based Person Re-Identification. Retrieved from http://arxiv.org/abs/1912.11236

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Unsupervised Representation Learning by Predicting Random Distances

Wang, H., Pang, G., Shen, C., & Ma, C. (2019). Unsupervised Representation Learning by Predicting Random Distances. Retrieved from http://arxiv.org/abs/1912.12186

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Few-shot Action Recognition via Improved Attention with Self-supervision

Zhang, H., Zhang, L., Qi, X., Li, H., Torr, P. H. S., & Koniusz, P. (2020). Few-shot Action Recognition via Improved Attention with Self-supervision. Retrieved from http://arxiv.org/abs/2001.03905

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Sampling Good Latent Variables via CPP-VAEs: VAEs with Condition Posterior as Prior

Aliakbarian, S., Saleh, F. S., Salzmann, M., Petersson, L., & Gould, S. (2019). Sampling Good Latent Variables via CPP-VAEs: VAEs with Condition Posterior as Prior. Retrieved from http://arxiv.org/abs/1912.08521

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Transductive Zero-Shot Learning for 3D Point Cloud Classification

Cheraghian, A., Rahman, S., Campbell, D., & Petersson, L. (2019). Transductive Zero-Shot Learning for 3D Point Cloud Classification. Retrieved from http://arxiv.org/abs/1912.07161

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An Equivariant Observer Design for Visual Localisation and Mapping

van Goor, P., Mahony, R., Hamel, T., & Trumpf, J. (2019). An Equivariant Observer Design for Visual Localisation and Mapping. Retrieved from http://arxiv.org/abs/1904.02452

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Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces

Felix, R., Harwood, B., Sasdelli, M., & Carneiro, G. (2019). Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces. Retrieved from http://arxiv.org/abs/1908.02013

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Neural Memory Networks for Robust Classification of Seizure Type

Ahmedt-Aristizabal, D., Fernando, T., Denman, S., Petersson, L., Aburn, M. J., & Fookes, C. (2019). Neural Memory Networks for Robust Classification of Seizure Type. Retrieved from http://arxiv.org/abs/1912.04968

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MTRNet++: One-stage Mask-based Scene Text Eraser

Tursun, O., Denman, S., Zeng, R., Sivapalan, S., Sridharan, S., & Fookes, C. (2019). MTRNet++: One-stage Mask-based Scene Text Eraser. Retrieved from http://arxiv.org/abs/1912.07183

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Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks

Dasagi, V., Lee, R., Bruce, J., & Leitner, J. (2019). Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks. Retrieved from http://arxiv.org/abs/1911.08666

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Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks

Fernando, T., Fookes, C., Denman, S., & Sridharan, S. (2019). Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks. Retrieved from http://arxiv.org/abs/1911.07844

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Improved Visual Localization via Graph Smoothing

Lassance, C., Latif, Y., Garg, R., Gripon, V., & Reid, I. (2019). Improved Visual Localization via Graph Smoothing. Retrieved from http://arxiv.org/abs/1911.02961

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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

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Question-Agnostic Attention for Visual Question Answering

Farazi, M. R., Khan, S. H., & Barnes, N. (2019). Question-Agnostic Attention for Visual Question Answering. Retrieved from http://arxiv.org/abs/1908.03289

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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

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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

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Unsupervised Task Design to Meta-Train Medical Image Classifiers

Maicas, G., Nguyen, C., Motlagh, F., Nascimento, J. C., & Carneiro, G. (2019). Unsupervised Task Design to Meta-Train Medical Image Classifiers. Retrieved from https://arxiv.org/pdf/1907.07816

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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

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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

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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

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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

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Optimal Feature Transport for Cross-View Image Geo-Localization

Shi, Y., Yu, X., Liu, L., Zhang, T., & Li, H. (2019). Optimal Feature Transport for Cross-View Image Geo-Localization.

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Densely Residual Laplacian Super-Resolution

Anwar, S., & Barnes, N. (2019). Densely Residual Laplacian Super-Resolution. Retrieved from http://arxiv.org/abs/1906.12021

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Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Purkait, P., Zach, C., & Reid, I. (2019). Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions. Retrieved from https://arxiv.org/pdf/1906.02885

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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

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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

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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

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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

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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

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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

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Event-based Star Tracking via Multiresolution Progressive Hough Transforms

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

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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

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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

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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

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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

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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

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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

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Training Quantized Neural Networks with the Full-precision Auxiliary Module

Zhuang, B., Liu, L., Tan, M., Shen, C., & Reid, I. (2019). Training Quantized Neural Networks with the Full-precision Auxiliary Module. Retrieved from http://arxiv.org/abs/1903.11236

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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

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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

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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

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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

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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

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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/

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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