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I am currently a PhD student at The University of Texas at Austin, advised by Philipp Krähenbühl. I am also a research intern at NVIDIA Research Autonomous Vehicle Research Group led by Dr. Marco Pavone . Prior to my current position, I worked happily with Prof. Kristen Grauman and Prof. Bolei Zhou as a research assistant. I was also fortunate to have worked as a research intern at Uber ATG, advised by Prof. Raquel Urtasun and Prof. Shenlong Wang. Previously, I had the luck to work with Prof. Kate Saenko and Dr. Xingchao Peng at Boston University and Prof. Wei-Shi Zheng at iSEE, Sun Yat-sen University. I obtained my bachelor's degree from Sun Yat-Sen University in 2021.My research interest mostly lies in machine learning and computer vision for autonomous driving, and particularly focuses on content generation for the safety of autonomous driving. My goal is to make autonomous driving safe and easily accessible to everyone. |
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The University of Texas at Austin
Sun Yat-Sen University |
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NVIDIA Research Autonomous Vehicle Research Group
The University of Texas at Austin
The Chinese University of Hong Kong
Uber ATG Toronto
Boston University
Sun Yat-Sen University |
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[09/2023] EgoDistill is accepted to NeurIPS 2023. [08/2023] LCTGen is accepted to CoRL 2023. [08/2023] I started my internship at NVIDIA. [01/2023] TrafficGen is accepted to ICRA 2023. [08/2021] I started my PhD study at UT Austin. |
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Shuhan Tan, Boris Ivanovic, Xinshuo Weng, Marco Pavone, Philipp Krähenbühl CoRL 2023. Paper / Project page / Code / Video / Demo (Colab) Traffic scene generation with language condition using LLM. |
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Shuhan Tan, Tushar Nagarajan, Kristen Grauman NeurIPS 2023. Efficient egocentric video understanding with head motion data from IMU. |
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Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan, Bolei Zhou ICRA 2023. Synthesis new traffic scenario and replay in simulation. |
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Shuhan Tan, Yujun Shen, Bolei Zhou. arXiv.2012.04842 preprint Paper / Project page / Colab Mitigate biases of GAN models without retraining. |
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Shuhan Tan*, Kelvin Wong*, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun. CVPR 2021. Generate realistic traffic scences automatically. |
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Shuhan Tan, Xingchao Peng, Kate Saenko. TASK-CV Workshop, ECCV 2020. Align feature distributions across domains while the label distributions of the two domains are also different. |
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Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma and Raquel Urtasun. CVPR 2020 (Oral). Paper / Supplement / Bibtex Realistic sensor simulation for LiDAR. |
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We proposed a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training. |
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Paper / Supplement / Bibtex We proposed a practical weakly supervised setting for open-set domain adaptation, where two scarcely-labeled domains collaboratively learn from each other. Invited for presentation at WebVision 2019. |
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Distinguished Graduate Thesis, Sun Yat-sen University SenseTime Scholarship (21 out of all AI-focus undergraduate students in China) |
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Systems and Methods for Simulating Traffic Scenes. US Patent App. 17/528,277.
Shuhan Tan, Kelvin Ka Wing Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun |
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Reviewer: CVPR, ECCV, ICCV, ICRA, RA-L, TCSVT, TMI. |
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Updated Oct 2023. Special thanks to Jon Barron for website template. |