Justin Liang
Justin Liang is a research scientist at Uber ATG Toronto. His research focuses on computer vision and machine learning for mapping and detection in self driving vehicles. Before joining ATG, he completed a MSc in Computer Science, supervised by Raquel Urtasun at the University of Toronto. He also has a BASc in Mechanical Engineering from the University of British Columbia.
Research Papers
DAGMapper: Learning to Map by Discovering Lane Topology
N. Homayounfar, W.-C. Ma\*, J. Liang\*, X. Wu, J. Fan, R. Urtasun
We map complex lane topologies in highways by formulating the problem as a deep directed graphical model. As an interesting result, we can train our model in I40 and generalize to unseen highways in SF. [PDF]
International Conference on Computer Vision (ICCV), 2019
We map complex lane topologies in highways by formulating the problem as a deep directed graphical model. As an interesting result, we can train our model in I40 and generalize to unseen highways in SF. [PDF]
International Conference on Computer Vision (ICCV), 2019
Convolutional Recurrent Network for Road Boundary Extraction
J. Liang, N. Homayounfar, S. Wang, W.-C. Ma, R. Urtasun
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Creating high definition maps that contain precise information of static elements of the scene is of utmost importance for enabling self driving cars to drive safely. In this paper, we tackle the problem of drivable road boundary extraction from LiDAR and camera imagery. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
End-to-End Deep Structured Models for Drawing Crosswalks
J. Liang, R. Urtasun
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple Li-DAR sweeps and the corresponding imagery, we project both inputs onto the ground surface to produce a top down view of the scene. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple Li-DAR sweeps and the corresponding imagery, we project both inputs onto the ground surface to produce a top down view of the scene. [...] [PDF]
European Conference on Computer Vision (ECCV), 2018
TorontoCity: Seeing the World With a Million Eyes
S. Wang; M. Bai; G. Mattyus; H. Chu; W. Luo; B. Yang; J. Liang; J. Cheverie; R. Urtasun; D. Lin.
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. [...] [PDF]
International Conference on Computer Vision (ICCV), 2017
Despite the substantial progress in recent years, the image captioning techniques are still far from being perfect. Sentences produced by existing methods, e.g. those based on RNNs, are often overly rigid and lacking in variability. [...] [PDF]
International Conference on Computer Vision (ICCV), 2017