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Ian Reid
Semantics for Robotic Mapping, Perception and Interaction: A Survey

Jan 02, 2021
Sourav Garg, Niko Sünderhauf, Feras Dayoub, Douglas Morrison, Akansel Cosgun, Gustavo Carneiro, Qi Wu, Tat-Jun Chin, Ian Reid, Stephen Gould, Peter Corke, Michael Milford

* Foundations and Trends in Robotics: Vol. 8: No. 1-2, pp 1-224 (2020) 
 * 81 pages, 1 figure, published in Foundations and Trends in Robotics, 2020 
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MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos

Dec 09, 2020
Kejie Li, Hamid Rezatofighi, Ian Reid


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EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels

Nov 11, 2020
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

* Paper accepted at WACV'21: Winter Conference on Applications of Computer Vision 
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HM4: Hidden Markov Model with Memory Management for Visual Place Recognition

Nov 01, 2020
Anh-Dzung Doan, Yasir Latif, Tat-Jun Chin, Ian Reid

* Accepted for publication by IEEE Robotics and Automation Letters 
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MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking

Oct 15, 2020
Patrick Dendorfer, Aljoša Ošep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, Laura Leal-Taixé

* Accepted at IJCV 
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How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?

Aug 08, 2020
Hamid Rezatofighi, Tran Thien Dat Nguyen, Ba-Ngu Vo, Ba-Tuong Vo, Silvio Savarese, Ian Reid


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Socially and Contextually Aware Human Motion and Pose Forecasting

Jul 14, 2020
Vida Adeli, Ehsan Adeli, Ian Reid, Juan Carlos Niebles, Hamid Rezatofighi

* Accepted in RA-L and IROS 
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Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos

Jul 06, 2020
Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh, Javen Shi, Ian Reid, Hamid Rezatofighi

* Accepted in the European Conference On Computer Vision (ECCV) 2020 
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Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue

Jun 04, 2020
Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, Ian Reid

* See codes, data, and demos in GitHub page (https://github.com/JiawangBian/Unsupervised-Indoor-Depth) 
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FroDO: From Detections to 3D Objects

May 11, 2020
Kejie Li, Martin Rünz, Meng Tang, Lingni Ma, Chen Kong, Tanner Schmidt, Ian Reid, Lourdes Agapito, Julian Straub, Steven Lovegrove, Richard Newcombe

* To be published in CVPR 2020. The first two authors contributed equally 
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MOT20: A benchmark for multi object tracking in crowded scenes

Mar 19, 2020
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, Laura Leal-Taixé

* The sequences of the new MOT20 benchmark were previously presented in the CVPR 2019 tracking challenge ( arXiv:1906.04567 ). The differences between the two challenges are: - New and corrected annotations - New sequences, as we had to crop and transform some old sequences to achieve higher quality in the annotations. - New baselines evaluations and different sets of public detections 
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3D Gated Recurrent Fusion for Semantic Scene Completion

Feb 17, 2020
Yu Liu, Jie Li, Qingsen Yan, Xia Yuan, Chunxia Zhao, Ian Reid, Cesar Cadena

* 13 pages 
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Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning

Feb 11, 2020
Haokui Zhang, Yu Liu, Bei Fang, Ying Li, Lingqiao Liu, Ian Reid

* 8 pages 
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Switchable Precision Neural Networks

Feb 07, 2020
Luis Guerra, Bohan Zhuang, Ian Reid, Tom Drummond


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Automatic Pruning for Quantized Neural Networks

Feb 03, 2020
Luis Guerra, Bohan Zhuang, Ian Reid, Tom Drummond


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Depth Based Semantic Scene Completion with Position Importance Aware Loss

Jan 30, 2020
Yu Liu, Jie Li, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid, Cesar Cadena

* ICRA2020 In Conjuction With RAL 
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Learn to Predict Sets Using Feed-Forward Neural Networks

Jan 30, 2020
Hamid Rezatofighi, Roman Kaskman, Farbod T. Motlagh, Qinfeng Shi, Anton Milan, Daniel Cremers, Laura Leal-Taixé, Ian Reid

* arXiv admin note: substantial text overlap with arXiv:1805.00613 
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Learning to generate new indoor scenes

Dec 10, 2019
Pulak Purkait, Christopher Zach, Ian Reid


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NeuRoRA: Neural Robust Rotation Averaging

Dec 10, 2019
Pulak Purkait, Tat-Jun Chin, Ian Reid


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Improved Visual Localization via Graph Smoothing

Nov 07, 2019
Carlos Lassance, Yasir Latif, Ravi Garg, Vincent Gripon, Ian Reid


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Visual Odometry Revisited: What Should Be Learnt?

Oct 03, 2019
Huangying Zhan, Chamara Saroj Weerasekera, Jiawang Bian, Ian Reid

* Demo video: https://youtu.be/Nl8mFU4SJKY Code: https://github.com/Huangying-Zhan/DF-VO 
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Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

Oct 03, 2019
Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid

* Accepted to NeurIPS 2019. Code is available at https://github.com/JiawangBian/SC-SfMLearner-Release 
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Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation

Sep 28, 2019
Yu Liu, Lingqiao Liu, Haokui Zhang, Hamid Rezatofighi, Ian Reid

* 9 pages 
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Structured Binary Neural Networks for Image Recognition

Sep 22, 2019
Bohan Zhuang, Chunhua Shen, Mingkui Tan, Lingqiao Liu, Ian Reid

* 15 pages. Extended version of the conference version arXiv:1811.10413 
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An Evaluation of Feature Matchers for Fundamental Matrix Estimation

Sep 11, 2019
Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid

* Accepted to British Machine Vision Conference (BMVC) 2019 
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An Evaluation of Feature Matchers forFundamental Matrix Estimation

Aug 26, 2019
Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid

* The paper has been accepted by British Machine Vision Conference (BMVC) 2019 
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In defense of OSVOS

Aug 20, 2019
Yu Liu, Yutong Dai, Anh-Dzung Doan, Lingqiao Liu, Ian Reid


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

Aug 10, 2019
Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, Chunhua Shen

* 12 pages. Extended version of B. Zhuang, C. Shen, M. Tan, L. Liu, and I. Reid, 'Towards effective low-bitwidth convolutional neural networks,' in Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2018. arXiv admin note: text overlap with arXiv:1711.00205 
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