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Dr. Kaiyang Zhou is an Assistant Professor at the Department of Computer Science, Hong Kong Baptist University, working on computer vision and machine learning. He was previously a postdoc at Nanyang Technological University, Singapore, working with Prof. Ziwei Liu and Prof. Chen Change Loy. He received his PhD in computer science from the University of Surrey, UK, under the supervision of Prof. Tao Xiang. His research has been published at top-tier journals and conferences in relevant fields, such as TPAMI, IJCV, and CVPR, with over 5,000 citations. He is an associate editor of the International Journal of Computer Vision (IJCV).

Research interests

• Foundation models: large vision/language models, multimodal models, prompting methods
• Computer vision: perception (classification, detection, and segmentation), image generation
• Machine learning: domain generalization, domain adaptation, transfer learning

News

• [2023-12] Invited to serve as ECCV 2024 area chair.
• [2023-10] Gave a talk at VALSE.
• [2023-08] Gave a talk at AIGC-2023 Workshop on Trustworthy Foundation Models under Imperfect Data.
• [2023-08] Gave a talk at IJCAI-2023 Symposium Session on Medical Large Models.
• [2023-08] Invited to serve as CVPR 2024 area chair.
• [2023-08] Invited to join the editorial board of International Journal of Computer Vision as associate editor.
• [2023-07] Gave a talk at the University of Tokyo (slides & video).
• [2023-06] We're organizing a tutorial on Prompting in Vision at CVPR 2023.
• [2023-01] Call for Papers: ICLR 2023 workshop on what do we need for successful domain generalization?
• [2022-09] Call for Papers: IJCV Special Issue on The Promises and Dangers of Large Vision Models.

Professional services

• Associate Editor: International Journal of Computer Vision (IJCV)
• Guest Editor: IJCV Special Issue on The Promises and Dangers of Large Vision Models
• Area Chair / Senior Program Committee: ECCV 2024, CVPR 2024, BMVC 2022, AAAI 2023-24
• Organizing Committee: CVPR 2023 Tutorial, ICLR 2023 Workshop, The AI Talks
• Reviewer: TPAMI, IJCV, ICLR, NeurIPS, ICML, AAAI, CVPR, ICCV, ECCV, etc.

Software and datasets

• Torchreid: A codebase for person re-identification (including documentation and model zoo).
• Dassl: A multifunctional codebase for domain generalization, domain adaptation and semi-supervised learning.
• CoOp: A codebase for developing adaptation methods (e.g., prompt learning) for large-scale vision-language models.
• OpenOOD: A codebase and benchmark for out-of-distribution detection.
• PSG: A dataset for panoptic scene graph generation. (Codebase: OpenPSG.)