Chaolei Tan

I'm a first-year Ph.D. student at Dept. of Computer Science & Engineering (CSE), The Hong Kong University of Science and Technology (HKUST), advised by Prof. Long Chen. I obtained my Master's degree from Sun Yat-sen University (SYSU) in 2024, where I was supervised by Prof. Jian-Fang Hu and Prof. Wei-Shi Zheng. Prior to that, I also received my Bachelor's degree from SYSU in 2021.

My research interests lie in computer vision, machine learning and multimedia, with a special focus on developing models that can learn general and high-level knowledge about the world from multi-modality data like videos and language.

Email / Scholar

News

2025-06: Two papers are accepted to ICCV 2025.

2024-12: Two papers are accepted to ACM MM 2024 and AAAI 2025.

2024-02: Two papers are accepted to CVPR 2024.

2023-02: Two papers are accepted to CVPR 2023.

2022-05: I win the championship of PIC Challenge HCVG Track at ACM MM 2022.

2021-06: I receive the Best Paper Award of CVPR 2021 PIC Workshop.

Selected Publications & Preprints

* indicates equal contributions. See the full list in Google Scholar

	SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses Chaolei Tan, Zihang Lin, Junfu Pu, Zhongang Qi, Wei-Yi Pei, Zhi Qu, Yexin Wang, Ying Shan, Wei-Shi Zheng, Jian-Fang Hu ACM International Conference on Multimedia (ACM MM), 2024 Paper/ Website A large-scale video-text dataset with densely annotated paragraph timestamps to enable the new research direction of multi-paragraph video grounding on both long-form videos and long-term queries.
	Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding Chaolei Tan, Jianhuang Lai, Wei-Shi Zheng, Jian-Fang Hu Computer Vision and Pattern Recognition (CVPR), 2024 Paper First attempt to explore weakly-supervised setting of video paragraph grounding, where a siamese learning framework jontly conducting feature alignment and boundary regression is proposed.
	Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding Chaolei Tan, Zihang Lin, Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai Computer Vision and Pattern Recognition (CVPR), 2023 Paper Introducing hierarchical modeling into video paragraph grounding by hierarchically aligning semantic correspondence across videos and paragraphs for temporal decoding at multiple granularities.
	Long-RVOS: A Comprehensive Benchmark for Long-term Referring Video Object Segmentation Tianming Liang, Haichao Jiang, Yuting Yang, Chaolei Tan, Shuai Li, Wei-Shi Zheng, Jian-Fang Hu ArXiv Preprint, 2025 Paper/ Website A new dataset for segmenting objects in long-form videos that jointly considers static, dynamic, and hybrid types of referring expressions.
	Enhancing Partially Relevant Video Retrieval with Hyperbolic Learning Jun Li, Jinpeng Wang, Chaolei Tan, Niu Lian, Long Chen, Yaowei Wang, Min Zhang, Shu-Tao Xia, Bin Chen International Conference on Computer Vision (ICCV), 2025 Paper/ Code Unleashing the power of hyperbolic cross-modal learning to achieve long-form text-video retrieval with partially relevant language query.
	ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations Tianming Liang, Kun-Yu Lin, Chaolei Tan, Jianguo Zhang, Wei-Shi Zheng, Jian-Fang Hu International Conference on Computer Vision (ICCV), 2025 Paper/ Website Our first attempt to adapt pretrained visual grounding foundation models to Referring Video Object Segmentation (RVOS).
	Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels Tianming Liang, Chaolei Tan, Beihao Xia, Wei-Shi Zheng, Jian-Fang Hu Computer Vision and Pattern Recognition (CVPR), 2024 Paper Tackle the incomplete annotation issues in open-ended video question answering with ranking distillation.
	Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding Zihang Lin, Chaolei Tan, Jian-Fang Hu, Zhi Jin, Tiancai Ye, Wei-Shi Zheng Computer Vision and Pattern Recognition (CVPR), 2023 Paper To model the collaborative static and dynamic vision-language streams for better spatio-temporal video grounding.