Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Ian Reid

Ian Reid

JRDB-Act: A Large-scale Multi-modal Dataset for Spatio-temporal Action, Social Group and Activity Detection


Jun 16, 2021
Mahsa Ehsanpour, Fatemeh Saleh, Silvio Savarese, Ian Reid, Hamid Rezatofighi

Figure 1 for JRDB-Act: A Large-scale Multi-modal Dataset for Spatio-temporal Action, Social Group and Activity Detection
Figure 2 for JRDB-Act: A Large-scale Multi-modal Dataset for Spatio-temporal Action, Social Group and Activity Detection

  Access Paper or Ask Questions

Unsupervised Scale-consistent Depth Learning from Video


May 25, 2021
Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

Figure 1 for Unsupervised Scale-consistent Depth Learning from Video
Figure 2 for Unsupervised Scale-consistent Depth Learning from Video
Figure 3 for Unsupervised Scale-consistent Depth Learning from Video
Figure 4 for Unsupervised Scale-consistent Depth Learning from Video
* Accept to IJCV. The source code is available at https://github.com/JiawangBian/SC-SfMLearner-Release 
  Access Paper or Ask Questions

TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild


Apr 08, 2021
Vida Adeli, Mahsa Ehsanpour, Ian Reid, Juan Carlos Niebles, Silvio Savarese, Ehsan Adeli, Hamid Rezatofighi

Figure 1 for TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Figure 2 for TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Figure 3 for TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild
Figure 4 for TRiPOD: Human Trajectory and Pose Dynamics Forecasting in the Wild

  Access Paper or Ask Questions

ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning


Mar 21, 2021
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Figure 1 for ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Figure 2 for ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Figure 3 for ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Figure 4 for ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning

  Access Paper or Ask Questions

Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging


Mar 16, 2021
Álvaro Parra, Shin-Fang Chng, Tat-Jun Chin, Anders Eriksson, Ian Reid

Figure 1 for Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Figure 2 for Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Figure 3 for Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
Figure 4 for Rotation Coordinate Descent for Fast Globally Optimal Rotation Averaging
* Accepted to CVPR 2021 as an oral presentation 
  Access Paper or Ask Questions

LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment


Mar 06, 2021
Filipe R. Cordeiro, Ragav Sachdeva, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Figure 1 for LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Figure 2 for LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Figure 3 for LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Figure 4 for LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment

  Access Paper or Ask Questions

Noisy Label Learning for Large-scale Medical Image Classification


Mar 06, 2021
Fengbei Liu, Yu Tian, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Figure 1 for Noisy Label Learning for Large-scale Medical Image Classification
Figure 2 for Noisy Label Learning for Large-scale Medical Image Classification
Figure 3 for Noisy Label Learning for Large-scale Medical Image Classification
Figure 4 for Noisy Label Learning for Large-scale Medical Image Classification

  Access Paper or Ask Questions

Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification


Mar 05, 2021
Fengbei Liu, Yu Tian, Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Figure 1 for Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Figure 2 for Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Figure 3 for Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Figure 4 for Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification

  Access Paper or Ask Questions

DF-VO: What Should Be Learnt for Visual Odometry?


Mar 01, 2021
Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian, Ravi Garg, Ian Reid

Figure 1 for DF-VO: What Should Be Learnt for Visual Odometry?
Figure 2 for DF-VO: What Should Be Learnt for Visual Odometry?
Figure 3 for DF-VO: What Should Be Learnt for Visual Odometry?
Figure 4 for DF-VO: What Should Be Learnt for Visual Odometry?
* extended version of ICRA-2020 paper (Visual Odometry Revisited: What Should Be Learnt?) 
  Access Paper or Ask Questions

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

Figure 1 for Semantics for Robotic Mapping, Perception and Interaction: A Survey
Figure 2 for Semantics for Robotic Mapping, Perception and Interaction: A Survey
* 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 
  Access Paper or Ask Questions

MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos


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

Figure 1 for MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos
Figure 2 for MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos
Figure 3 for MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos
Figure 4 for MO-LTR: Multiple Object Localization, Tracking, and Reconstruction from Monocular RGB Videos

  Access Paper or Ask Questions

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

Figure 1 for EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Figure 2 for EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Figure 3 for EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Figure 4 for EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
* Paper accepted at WACV'21: Winter Conference on Applications of Computer Vision 
  Access Paper or Ask Questions

HM4: Hidden Markov Model with Memory Management for Visual Place Recognition


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

Figure 1 for HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Figure 2 for HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Figure 3 for HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
Figure 4 for HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
* Accepted for publication by IEEE Robotics and Automation Letters 
  Access Paper or Ask Questions

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

Figure 1 for MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
Figure 2 for MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
Figure 3 for MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
Figure 4 for MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking
* Accepted at IJCV 
  Access Paper or Ask Questions

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

Figure 1 for How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?
Figure 2 for How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?
Figure 3 for How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?
Figure 4 for How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?

  Access Paper or Ask Questions

Socially and Contextually Aware Human Motion and Pose Forecasting


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

Figure 1 for Socially and Contextually Aware Human Motion and Pose Forecasting
Figure 2 for Socially and Contextually Aware Human Motion and Pose Forecasting
Figure 3 for Socially and Contextually Aware Human Motion and Pose Forecasting
Figure 4 for Socially and Contextually Aware Human Motion and Pose Forecasting
* Accepted in RA-L and IROS 
  Access Paper or Ask Questions

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

Figure 1 for Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Figure 2 for Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Figure 3 for Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Figure 4 for Joint learning of Social Groups, Individuals Action and Sub-group Activities in Videos
* Accepted in the European Conference On Computer Vision (ECCV) 2020 
  Access Paper or Ask Questions

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

Figure 1 for Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue
Figure 2 for Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue
Figure 3 for Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue
Figure 4 for Unsupervised Depth Learning in Challenging Indoor Video: Weak Rectification to Rescue
* See codes, data, and demos in GitHub page (https://github.com/JiawangBian/Unsupervised-Indoor-Depth) 
  Access Paper or Ask Questions

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

Figure 1 for FroDO: From Detections to 3D Objects
Figure 2 for FroDO: From Detections to 3D Objects
Figure 3 for FroDO: From Detections to 3D Objects
Figure 4 for FroDO: From Detections to 3D Objects
* To be published in CVPR 2020. The first two authors contributed equally 
  Access Paper or Ask Questions

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

Figure 1 for MOT20: A benchmark for multi object tracking in crowded scenes
Figure 2 for MOT20: A benchmark for multi object tracking in crowded scenes
Figure 3 for MOT20: A benchmark for multi object tracking in crowded scenes
Figure 4 for MOT20: A benchmark for multi object tracking in crowded scenes
* 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 
  Access Paper or Ask Questions

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

Figure 1 for 3D Gated Recurrent Fusion for Semantic Scene Completion
Figure 2 for 3D Gated Recurrent Fusion for Semantic Scene Completion
Figure 3 for 3D Gated Recurrent Fusion for Semantic Scene Completion
Figure 4 for 3D Gated Recurrent Fusion for Semantic Scene Completion
* 13 pages 
  Access Paper or Ask Questions

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

Figure 1 for Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Figure 2 for Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Figure 3 for Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Figure 4 for Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
* 8 pages 
  Access Paper or Ask Questions

Switchable Precision Neural Networks


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

Figure 1 for Switchable Precision Neural Networks
Figure 2 for Switchable Precision Neural Networks
Figure 3 for Switchable Precision Neural Networks
Figure 4 for Switchable Precision Neural Networks

  Access Paper or Ask Questions

Automatic Pruning for Quantized Neural Networks


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

Figure 1 for Automatic Pruning for Quantized Neural Networks
Figure 2 for Automatic Pruning for Quantized Neural Networks
Figure 3 for Automatic Pruning for Quantized Neural Networks
Figure 4 for Automatic Pruning for Quantized Neural Networks

  Access Paper or Ask Questions

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

Figure 1 for Depth Based Semantic Scene Completion with Position Importance Aware Loss
Figure 2 for Depth Based Semantic Scene Completion with Position Importance Aware Loss
Figure 3 for Depth Based Semantic Scene Completion with Position Importance Aware Loss
Figure 4 for Depth Based Semantic Scene Completion with Position Importance Aware Loss
* ICRA2020 In Conjuction With RAL 
  Access Paper or Ask Questions

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

Figure 1 for Learn to Predict Sets Using Feed-Forward Neural Networks
Figure 2 for Learn to Predict Sets Using Feed-Forward Neural Networks
Figure 3 for Learn to Predict Sets Using Feed-Forward Neural Networks
Figure 4 for Learn to Predict Sets Using Feed-Forward Neural Networks
* arXiv admin note: substantial text overlap with arXiv:1805.00613 
  Access Paper or Ask Questions

Learning to generate new indoor scenes


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

Figure 1 for Learning to generate new indoor scenes
Figure 2 for Learning to generate new indoor scenes
Figure 3 for Learning to generate new indoor scenes
Figure 4 for Learning to generate new indoor scenes

  Access Paper or Ask Questions

NeuRoRA: Neural Robust Rotation Averaging


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

Figure 1 for NeuRoRA: Neural Robust Rotation Averaging
Figure 2 for NeuRoRA: Neural Robust Rotation Averaging
Figure 3 for NeuRoRA: Neural Robust Rotation Averaging
Figure 4 for NeuRoRA: Neural Robust Rotation Averaging

  Access Paper or Ask Questions

Improved Visual Localization via Graph Smoothing


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

Figure 1 for Improved Visual Localization via Graph Smoothing
Figure 2 for Improved Visual Localization via Graph Smoothing
Figure 3 for Improved Visual Localization via Graph Smoothing
Figure 4 for Improved Visual Localization via Graph Smoothing

  Access Paper or Ask Questions

Visual Odometry Revisited: What Should Be Learnt?


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

Figure 1 for Visual Odometry Revisited: What Should Be Learnt?
Figure 2 for Visual Odometry Revisited: What Should Be Learnt?
Figure 3 for Visual Odometry Revisited: What Should Be Learnt?
Figure 4 for Visual Odometry Revisited: What Should Be Learnt?
* Demo video: https://youtu.be/Nl8mFU4SJKY Code: https://github.com/Huangying-Zhan/DF-VO 
  Access Paper or Ask Questions