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2019 Scientific Publications [134]

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

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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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Curiosity Did Not Kill the Robot. ACM Transactions on Human-Robot Interaction

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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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/

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

Dunbabin, M., Dayoub, F., Lamont, R., & Martin, S. (2019). Real-time Vision-only Perception for Robotic Coral Reef Monitoring and Management. Retrieved from http://icra-2019-uwroboticsperception.ge.issia.cnr.it/assets/ICRA19-WS-URP-CameraReadySubmissions/ICRA19-WS-URP-Paper-20

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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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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"

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Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes

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

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Visual SLAM: Why Bundle Adjust?

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

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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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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/

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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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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

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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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Learning robust, real-time, reactive robotic grasping

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

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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

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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

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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/

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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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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

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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

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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

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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

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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

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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

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

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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

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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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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

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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

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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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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"

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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"

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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"

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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"

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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

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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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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

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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

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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.

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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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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"

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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"

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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

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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

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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

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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

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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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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

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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

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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.

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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

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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

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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

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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

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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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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

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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

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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

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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

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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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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

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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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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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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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

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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

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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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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

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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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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

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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

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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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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

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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

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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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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

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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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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

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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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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

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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

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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

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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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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

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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

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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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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

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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

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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

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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

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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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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

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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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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

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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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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

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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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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

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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

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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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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

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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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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.

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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

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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

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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

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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

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