Bin Yang  

I'm a MSc student at University of Toronto. I'm a member of the Machine Learning group and my advisor is Prof. Raquel Urtasun. I also work at Uber Advanced Technologies Group (Toronto), doing research related to self-driving vehicles.

Email  /  CV  /  Google Scholar  /  GitHub  /  LinkedIn

Research

I'm interested in computer vision and machine learning. Currently I'm aiming at building holistic models for 3D scene understanding. When I first entered this amazing field, I focused on object detection.

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

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 2017).

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 (Special Issue on Large Scale and Nonlinear Similarity Learning for Intelligent Video Analysis), 2016
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 (*: equal contributions)
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]


This template comes from Jon Barron.