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Min Bai

Min Bai
0 BLOG ARTICLES 5 RESEARCH PAPERS
Min Bai is a research scientist at Uber ATG Toronto. Before that, he was a wireless systems engineer at Apple. He has an undergraduate degree in electrical engineering from the University of Waterloo. His research interest includes various perception tasks such as segmentation, point cloud processing, online mapping.

Research Papers

UPSNet: A Unified Panoptic Segmentation Network

Y. Xiong, R. Liao, H. Zhao, R. Hu, M. Bai, E. Yumer, R. Urtasun
In this paper we tackle the problem of scene flow estimation in the context of self-driving. We leverage deep learning techniques as well as strong priors as in our application domain the motion of the scene can be composed by the motion of the robot and the 3D motion of the actors in the scene. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019

Deep Multi-Sensor Lane Detection

M. Bai, G. Mattyus, N. Homayounfar, S. Wang, S. K. Lakshmikanth, R. Urtasun
Reliable and accurate lane detection has been a long-standing problem in the field of autonomous driving. In recent years, many approaches have been developed that use images (or videos) as input and reason in image space. In this paper we argue that accurate image estimates do not translate to precise 3D lane boundaries, which are the input required by modern motion planning algorithms. [...] [PDF]
International Conference on Intelligent Robots and Systems (IROS), 2018

Learning deep structured active contours end-to-end

D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, R. Liao, R. Urtasun
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 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

Deep Watershed Transform for Instance Segmentation

M. Bai, R. Urtasun
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In our paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. [...] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2017

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