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Long Chen

Assistant Professor
Department of Computer Science and Engineering (CSE)
School of Engineering (SENG)
The Hong Kong University of Science and Technology (HKUST)
Email: longchen A~T ust.hk
Office: Room 3508 (via Lift 25 & 26), Academic Building,
HKUST, Clear Water Bay, Kowloon, Hong Kong

Dr. Long CHEN (Chinese: 陈隆) is an assistant professor at the Department of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST) starting from 2023. He is leading the research group: LONG Group. Before joining HKUST, he was a postdoctoral research scientist at the DVMM Lab, Columbia University working with Prof. Shih-Fu Chang (2021 - 2023). He obtained his Ph.D. degree in Computer Science from Zhejiang University and his Ph.D. advisor is Prof. Jun Xiao (2015 - 2020). During his Ph.D. study period, he also worked closely with Prof. Hanwang Zhang from Nanyang Technological University (NTU) and Prof. Tat-Seng Chua from National University of Singapore (NUS). He obtained his B.Eng. degree in Electronic Information Engineering from Dalian University of Technology (2011 - 2015). He was a senior research scientist at Tencent AI Lab (2020 - 2021).

Research Group: LONG Group @ HKUST CSE

1. Based on the current funding situation, we have only extremely limited postdocs, research assistants, and visiting students openings. (Please also highlight if you have other funding sources or supports).
2. As for Ph.D. and M.Phil. positions, we always have the openings all year around.
3. To further increase the diversity, Ph.D./M.Phil applicants from overseas countries and HK are strongly recommended.


Recent Teaching


Research Interest

His primary research interests are Computer Vision, Machine Learning, and Multimedia. Specifically, he aims to build an efficient vision system that can understand complex visual scenes as humans. By “human-like”, we mean that the vision systems should be equipped with three types of abilities:

1) Explainable: The model should rely on (right) explicit evidences when making decisions, i.e., right for the right reasons.

2) Robust: The model should be robust to some situations with only low-quality training data (e.g., training samples are biased, noisy, or limited).

3) Universal: The model design is relatively universal, i.e., it is expected to be effective for various tasks.

Meanwhile, with the rapid development in pretrained models, such as the appearance of Large Language Models (LLMs), Stable Diffusion, we are also very interested in several releveant cutting-edge directions:

4) Building more explainable, robust, and universal vision models with the help of pretrained models (LLMs, diffusion models).

5) Designing more efficient and stronger multimodal LLMs.

6) The inherent weaknesses in existing LLMs and diffusion models.


News

Nov, 2024 I will serve as a Senior PC for IJCAI 2025.
Nov, 2024 Our research group has the 4th group outing activity: Hiking in MacLehose Trail (Section 2), again!.
Sep, 2024 I was ranked as the World’s Top 2% Most-cited Scientists (in the single year 2023) by Stanford University.
Sep, 2024 I will serve as an Area Chair for CVPR 2025.
Aug, 2024 I will serve as an Area Chair for ICLR 2025.
Jul, 2024 Two students have received HKUST RedBird PhD Awards. Congrats to Chaolei and Jiazhen!.
Jun, 2024 I will serve as a Senior PC for AAAI 2025.
May, 2024 I will serve as an Area Chair for NeurIPS 2024 and an Area Chair for BMVC 2024.
Apr, 2024 We will organize The 2nd Workshop on Deep Multimodal Generation and Retrieval in ACM Multimedia 2024.
Jan, 2024 I will serve as an Area Chair for ECCV 2024.
Jan, 2024 Our research group has the 2nd group outing activity: Hiking in Shek-O and Cape D'Aguilar.
Nov, 2023 I will serve as an Area Chair for ACM Multimedia 2024.

Recent Publications

  1. arXiv
    [New!!] Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
    Kaifeng Gao, Jiaxin Shi, Hanwang Zhang, Chunping Wang, Jun Xiao, and Long Chen
    arXiv preprint (arXiv) , arXiv , Codes
  2. arXiv
    [New!!] A Survey on Multimodal Benchmarks: In the Era of Large AI Models
    Lin Li, Guikun Chen, Hanrong Shi, Jun Xiao, and Long Chen
    arXiv preprint (arXiv) , arXiv , Codes
  3. arXiv
    [New!!] IterIS: Iterative Inference-Solving Alignment for LoRA Merging
    Hongxu Chen, Runshi Li, Bowei Zhu, Zhen Wang, and Long Chen
    arXiv preprint (arXiv) , arXiv
  4. arXiv
    [New!!] R3HF: Reward Redistribution for Enhancing Reinforcement Learning from Human Feedback
    Jiahui Li, Tai-Wei Chang, Fengda Zhang, Kun Kuang, and Long Chen
    arXiv preprint (arXiv) , arXiv
  5. arXiv
    [New!!] Event-Customized Image Generation
    Zhen Wang, Yilei Jiang, Dong Zheng, Jun Xiao, and Long Chen
    arXiv preprint (arXiv) , arXiv
  6. arXiv
    [New!!] Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification
    Yanghao Wang, and Long Chen
    arXiv preprint (arXiv) , arXiv , Codes
  7. arXiv
    [New!!] Combining Text-based and Drag-based Editing for Precise and Flexible Image Editing
    Ziqi Jiang, Zhen Wang, and Long Chen
    arXiv preprint (arXiv) , arXiv
  8. arXiv
    [New!!] CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
    Wei Chen, Lin Li, Yongqi Yang, Bin Wen, Fan Yang, Tingting Gao, Yu Wu, and Long Chen
    arXiv preprint (arXiv) , arXiv , Codes
  9. arXiv
    [New!!] FreeTuner: Any Subject in Any Style with Training-free Diffusion
    Youcan Xu, Zhen Wang, Jun Xiao, Wei Liu, and Long Chen
    arXiv preprint (arXiv) , arXiv
  10. NeurIPS
    [New!!] Di2Pose: Discrete Diffusion Model for Occluded 3D Human Pose Estimation
    Weiquan Wang, Jun Xiao, Chunping Wang, Wei Liu, Zhao Wang, and Long Chen
    Neural Information Processing Systems (NeurIPS) , 2024
  11. NeurIPS
    [New!!] LLMs Can Evolve Continually on Modality for X-Modal Reasoning
    Jiazuo Yu, Haomiao Xiong, Lu Zhang, Haiwen Diao, Yunzhi Zhuge, Lanqing Hong, Dong Wang, Huchuan Lu, You He, and Long Chen
    Neural Information Processing Systems (NeurIPS) , 2024 , Codes
  12. EMNLP
    [New!!] Optimizing Language Models with Fair and Stable Reward Composition in Reinforcement Learning
    Jiahui Li, Hanlin Zhang, Fengda Zhang, Tai-Wei Chang, Kun Kuang, Long Chen, and Jun Zhou
    Empirical Methods in Natural Language Processing (EMNLP) , 2024
  13. ECCV
    View-Consistent 3D Editing with Gaussian Splatting
    Yuxuan Wang, Xuanyu Yi, Zike Wu, Na Zhao, Long Chen, and Hanwang Zhang
    European Conference on Computer Vision (ECCV) , 2024 , Website
  14. ECCV
    DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism
    Zhen Wang, Xinyun Jiang, Jun Xiao, Tao Chen, and Long Chen
    European Conference on Computer Vision (ECCV) , 2024
  15. CVPR
    UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory
    Haiwen Diao, Bo Wan, Ying Zhang, Xu Jia, Huchuan Lu, and Long Chen
    Computer Vision and Pattern Recognition (CVPR) , 2024 , Codes
  16. ICLR
    SCHEMA: State CHangEs MAtter for Procedure Planning in Instructional Videos
    Yulei Niu, Wenliang Guo, Long Chen, Xudong Lin, and Shih-Fu Chang
    International Conference on Learning Representations (ICLR) , 2024 , Codes
  17. TPAMI
    A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
    Chaoyang Zhu, and Long Chen
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 2024 , Codes
  18. TPAMI
    NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation
    Lin Li, Jun Xiao, Hanrong Shi, Hanwang Zhang, Yi Yang, Wei Liu, and Long Chen
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 2024 , Codes , extension of CVPR’22 work
  19. TPAMI
    CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attention
    Wenxiao Wang, Wei Chen, Qibo Qiu, Long Chen, Boxi Wu, Binbin Lin, Xiaofei He, and Wei Liu
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 2024 , Codes , extension of ICLR’22 work