Bin Yang  

I'm a PhD student at Department of Computer Science, University of Toronto since 2018. I'm a member of the Machine Learning Group and my advisor is Prof. Raquel Urtasun.

I'm also a Senior Research Scientist at Uber Advanced Technologies Group (Toronto), doing research related to 3D perception for autonomous vehicles.

I obtained my master's degree from UofT in 2018, and my bachelor's degree from China Agricultural University in 2014. From 2014 to 2016, I was very fortunate to have internships at NLPR, CASIA under supervision of Prof. Stan Z. Li and Prof. Zhen Lei, and at SenseTime hosted by Dr. Junjie Yan.

Email  /  Google Scholar  /  GitHub  /  LinkedIn

News

  • Code release for SBNet, example reweight.
  • 2 papers (1 oral) have been accepted by CVPR2019.
  • 6 papers (2 orals, 2 spotlights) have been accepted in 2018 by CVPR/ICML/ECCV/CoRL.

  • Research

    My general research interest lies in computer vision and machine learning, with a recent focus on deep learning algorithms for perception, prediction and planning in autonomous driving scenario.

    Multi-Task Multi-Sensor Fusion for 3D Object Detection
    Ming Liang*, Bin Yang*, Yun Chen, Rui Hu, Raquel Urtasun
    Computer Vision and Pattern Recognition (CVPR), 2019

    Multi-sensor fusion <==> multi-task learning.

    We got 1st place on KITTI 2D/3D/BEV car detection leaderboard.

    End-to-end Interpretable Neural Motion Planner
    Wenyuan Zeng*, Wenjie Luo*, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun
    Computer Vision and Pattern Recognition (CVPR), 2019 (Oral)

    The first end-to-end learnable and interpretable motion planner.

    HDNET: Exploiting HD Maps for 3D Object Detection
    Bin Yang, Ming Liang, Raquel Urtasun
    2nd Conference on Robot Learning (CoRL), 2018 (Spotlight)

    A LiDAR based 3D detector that exploits geometric and semantic priors from HD maps (built offline or estimated online).

    We got 1st place on KITTI BEV car detection leaderboard.

    Deep Continuous Fusion for Multi-Sensor 3D Object Detection
    Ming Liang, Bin Yang, Shenlong Wang, Raquel Urtasun
    European Conference on Computer Vision (ECCV), 2018

    Geometry-aware dense feature fusion for high-performance Camera-LiDAR based 3D object detection.

    We got 1st place on KITTI BEV car detection leaderboard.

    Learning to Reweight Examples for Robust Deep Learning
    Mengye Ren, Wenyuan Zeng, Bin Yang, Raquel Urtasun
    International Conference on Machine Learning (ICML), 2018 (Oral)
    code

    Online example weighting algorithm for problems with imbalanced classes or noisy labels.

    PIXOR: Real-time 3D Object Detection From Point Clouds
    Bin Yang, Wenjie Luo, Raquel Urtasun
    Computer Vision and Pattern Recognition (CVPR), 2018
    FAQ / arXiv (new)

    A bird's-eye-view 3D detector that runs at 28 FPS.

    Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net
    Wenjie Luo, Bin Yang, Raquel Urtasun
    Computer Vision and Pattern Recognition (CVPR), 2018 (Oral)
    UofT News

    Joint detection, prediction and tracking in point clouds with a single CNN.

    SBNet: Sparse Blocks Network for Fast Inference
    Mengye Ren*, Andrei Pokrovsky*, Bin Yang*, Raquel Urtasun
    Computer Vision and Pattern Recognition (CVPR), 2018 (Spotlight)
    UBER blog / UofT News / NVIDIA Pioneer Award / code

    Speeding up inference by exploiting sparsity in CNN activations.

    TorontoCity: Seeing the World with a Million Eyes
    Shenlong Wang, Min Bai*, Gellért Máttyus*, Hang Chu*, Wenjie Luo, Bin Yang, Justin Liang, Joel Cheverie, Sanja Fidler, Raquel Urtasun
    International Conference on Computer Vision (ICCV), 2017 (Spotlight)

    City-scale benchmark dataset (covering the full Greater Toronto Area) that contains data in the form of aerial image, panorama, GoPro, LiDAR, as well as maps with 3D buildings and road information.

    Gated Bi-directional CNN for Object Detection
    Xingyu Zeng, Wanli Ouyang, Bin Yang, Junjie Yan, Xiaogang Wang
    European Conference on Computer Vision (ECCV), 2016
    project page / code

    Capturing multi-scale context with bi-directional message passing.

    Combined with CRAFT, we got 1st place in ILSVRC 2016 Object Detection Task (technical report accepted by TPAMI 2018).

    T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
    Kai Kang, Hongsheng Li, Junjie Yan, Xingyu Zeng, Bin Yang, Tong Xiao, Cong Zhang, Zhe Wang, Ruohui Wang, Xiaogang Wang, Wanli Ouyang
    IEEE Transactions on Circuits and Systems for Video Technology, 2018
    slides / code

    Using CRAFT and DeepID-Net as still-image object detectors, we got 1st place in ILSVRC 2015 Object Detection from Video Task.

    CRAFT Objects from Images
    Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
    Computer Vision and Pattern Recognition (CVPR), 2016
    project page / code

    Cascade in proposal! Cascade in detection!

    Convolutional Channel Features
    Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
    International Conference on Computer Vision (ICCV), 2015
    project page / video / code

    Convolutional maps + random forests = one approach for diverse tasks.

    Fine-grained Evaluation on Face Detection in the Wild
    Bin Yang*, Junjie Yan*, Zhen Lei, Stan Z. Li
    International Conference on Automatic Face and Gesture Recognition (FG), 2015
    project page

    AP_i = AP on testing faces with attribute_i (otherwise ignored)

    Adaptive Structural Model for Video Based Pedestrian Detection
    Junjie Yan, Bin Yang, Zhen Lei, Stan Z. Li
    Asian Conference on Computer Vision (ACCV), 2014

    An approach that adapts image-based pedestrian detector to videos.

    Aggregate Channel Features for Multi-view Face Detection
    Bin Yang, Junjie Yan, Zhen Lei, Stan Z. Li
    International Joint Conference on Biometrics (IJCB), 2014 (Oral, Best Student Paper)
    project page

    Real-time face detector with state-of-the-art performance on AFW and FDDB.
    My bachelor's thesis.

    Teaching Assistant

    Winter 2017, CSC411: Machine Learning and Data Mining
    Fall 2016, CSC420: Introduction to Image Understanding
        -  object detection tutorial [slides]

    Talks

    Winter 2017, CSC2541: Topics in Machine Learning - Sport Analytics
        -  intro to convnets [slides][demo codes]
        -  intro to object detection [slides]

    Winter 2018, CSC2548: Machine Learning in Computer Vision
        -  intro to object detection [slides]


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