Haodong Duan (段浩东)

I am a researcher at ByteDance Seed, working on the evaluation and development of large language models and large multi-modality models (LMMs). I received my Ph.D. degree from the Multimedia Laboratory (MMLab) at The Chinese University of Hong Kong in 2023, supervised by Professor Dahua Lin. Before that, I received my B.S. degree in Data Science from Peking University in 2019.
My research interests span multi-modal learning, LLM/LMM evaluation, and video understanding. I have led the development of several widely-used evaluation toolkits and benchmarks, including VLMEvalKit (4k+ stars), MMBench, and OpenCompass (6.8k+ stars).
  I am open to academic collaborations. Feel free to reach out via email.

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Selected Publications

* denotes equal contribution, † denotes corresponding author. See the full list on my Publications page or Google Scholar.

MMBench: Is Your Multi-modal Model an All-around Player?
Yuan Liu*, Haodong Duan*†, Yuanhan Zhang*, Bo Li*, et al.
European Conference on Computer Vision (ECCV), 2024 — Oral Presentation
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Haodong Duan†, Junming Yang, Yuxuan Qiao, et al.
ACM International Conference on Multimedia (MM), 2024
MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding
Xinyu Fang*, Kangrui Mao*, Haodong Duan*†, et al.
NeurIPS 2024 — Datasets & Benchmarks Track
Revisiting Skeleton-based Action Recognition (PoseC3D)
Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, Bo Dai
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 — Oral
PYSKL: Towards Good Practices for Skeleton Action Recognition
Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin
ACM International Conference on Multimedia (MM), 2022

Professional Activities

  • Conference Reviewer: ICCV (2021–2025), CVPR (2022–2025), NeurIPS (2022–2024), ECCV (2022–2024), AAAI (2022–2025), ICML (2023–2024), ICLR (2023–2025), WACV 2023, EuroGraphics 2023
  • Journal Reviewer: IEEE TPAMI, IJCV, IEEE TIP, Pattern Recognition, IEEE TMM

Open-Source Contributions