Biography
I am currently a final-year all-but-dissertation Ph.D. candidate from the Department of Computer Science at Duke University, expected to graduate in December 2023. My advisor is Professor Carlo Tomasi. Before coming to Duke, I received my B.Sc. degree in Computer Science and Technology in June 2018 from Nanjing University, where I did undergraduate research supervised by Professor Zhi-Hua Zhou at NJU LAMDA Group. For more information, please refer to my latest CV (2023-12-03 version).
Research Interests
I am interested in
Computer Vision and
Deep Learning. Currently, I mainly focus on the following topics:
- Video motion and tracking: optical flow, occlusion, and motion boundary estimation
- Video 3D scene geometry/SLAM: stereo matching, 3D rigid motion/pose, 3D reconstruction
- Generative AI: vision generative models, vision-language multi-modality
- Computer vision applications in autonomous driving and virtual/augmented/mixed reality
Work Experiences
- Research Intern, Meta Reality Labs, Core AI (Computational Photography) team. Seattle WA, USA. May 2023 - Dec 2023.
- Research Intern, Meta Reality Labs, Surreal team. Redmond WA, USA. May 2022 - Aug 2022.
- Research Intern, Facebook Reality Labs, Agios team. Redmond WA, USA. May 2020 - Aug 2020.
Publications
- Shuai Yuan, Shuzhi Yu, Hannah Kim, and Carlo Tomasi. SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous Driving. International Conference on Computer Vision (ICCV), 2023.
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- Shuai Yuan, Xian Sun, Hannah Kim, Shuzhi Yu, and Carlo Tomasi. Optical Flow Training under Limited Label Budget via Active Learning. European Conference on Computer Vision (ECCV), 2022.
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- Shuai Yuan and Carlo Tomasi. UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation. arXiv Preprint, 2023.
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- Fanjie Kong, Shuai Yuan, Weituo Hao, and Ricardo Henao. Mitigating Test-Time Bias for Fair Image Retrieval. Neural Information Processing Systems (NeurIPS), 2023.
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- Xiang Wang, Shuai Yuan, Chenwei Wu, and Rong Ge. Guarantees for Tuning the Step Size using a Learning-to-Learn Approach. International Conference on Machine Learning (ICML), 2021.
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- Shuzhi Yu, Hannah Kim, Shuai Yuan, and Carlo Tomasi. Unsupervised Flow Refinement near Motion Boundaries. British Machine Vision Conference (BMVC), 2022.
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- Hannah Kim, Shuzhi Yu, Shuai Yuan, and Carlo Tomasi. Cross-Attention Transformer for Video Interpolation. Asian Conference on Computer Vision (ACCV) - Workshop on Vision Transformers: Theory and Application, 2022. Best Paper Award.
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Reviewer Services
- Conferences: NeurIPS 2023 and ACCV 2022.
Teaching Assistantship
Personal Interests
- Sports/Workouts: gym training, swimming, hiking, badminton, table tennis, tennis, volleyball, etc.
- Handcrafting: lego-style blocks, mechanical, electronic, metal, crochet, etc.
- Drama/Musicals: Les Misérables, Dear Evan Hansen, Chinese original musicals, etc.