Wenyuan Zeng

University of Toronto — Canada

Engineering · Physical Sciences

19h-index2.9kcitations35works0.02yr avg
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Research Topics

Autonomous Vehicle Technology and Safety(19), Advanced Neural Network Applications(14), Video Surveillance and Tracking Methods(12), Domain Adaptation and Few-Shot Learning(6), Anomaly Detection Techniques and Applications(6)

Publications35 total

Rethinking Closed-Loop Training for Autonomous Driving
Lecture notes in computer science·2022· 20 citedOpen Access
Self-Supervised Representation Learning from Flow Equivariance
2021 IEEE/CVF International Conference on Computer Vision (ICCV)·2021· 23 cited
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)·2021· 191 cited
Safety-Oriented Pedestrian Occupancy Forecasting
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)·2021· 8 cited
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
2021· 1 citedOpen Access
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
2021· 33 cited
Deep Structured Reactive Planning
2021· 23 cited
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
arXiv (Cornell University)·2021· 1 citedOpen Access
Deep Structured Reactive Planning
arXiv (Cornell University)·2021· 1 citedOpen Access
Network Automatic Pruning: Start NAP and Take a Nap
arXiv (Cornell University)·2021· 3 citedOpen Access
Auto4D: Learning to Label 4D Objects from Sequential Point Clouds
arXiv (Cornell University)·2021· 25 citedOpen Access
End-to-end Interpretable Neural Motion Planner
arXiv (Cornell University)·2021· 4 citedOpen Access
Self-Supervised Representation Learning from Flow Equivariance
arXiv (Cornell University)·2021· 1 citedOpen Access
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
arXiv (Cornell University)·2021· 4 citedOpen Access
Weakly Supervised Object Detection Using Proposal- and Semantic-Level Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence·2020· 124 cited
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
arXiv (Cornell University)·2020· 4 citedOpen Access
End-to-end Contextual Perception and Prediction with Interaction Transformer
2020· 18 citedOpen Access
Weakly-supervised 3D Shape Completion in the Wild
arXiv (Cornell University)·2020· 5 citedOpen Access
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
arXiv (Cornell University)·2020· 14 citedOpen Access
DSDNet: Deep Structured self-Driving Network
arXiv (Cornell University)·2020· 12 citedOpen Access
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Frequent Co-authors

Raquel Urtasun(29), Mengye Ren(9), Yuwen Xiong(8), Sergio Casas(7), Ming Liang(7), Bin Yang(6), Sivabalan Manivasagam(5), Renjie Liao(5), Bin Yang(4), Yun Chen(4), Bin Yang(3), Wenjie Luo(3), Rui Hu(3), Shenlong Wang(3), Wei-Chiu Ma(3), Tsun-Hsuan Wang(2), Ming Liang(2), Simon Su(2), Abbas Sadat(2), Yankai Lin(2)