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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 Dept. of Computer Science and Engineering (CSE), Hong Kong University of Science and Technology (HKUST). 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. He obtained his Ph.D. degree in Computer Science from Zhejiang University and his Ph.D. advisor is Prof. Jun Xiao (Changjiang Scholar). During his Ph.D. study period, he also worked closely with Prof. Hanwang Zhang from Nanyang Technological University (NTU), Prof. Shih-Fu Chang from Columbia University, 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. He was a senior research scientist at Tencent AI Lab working with Dr. Wei Liu.

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.

We still have PhD/MPhil openings for 2025 intake. Unfortunately, due to my recent extremely busy schedule, I haven't got time to reply relevant emails or schedule interviews. Sorry for the late responses :(


Recent Teaching


Research Interest

His primary research interest 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

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 Three students have received HKUST RedBird PhD Awards. Congrats to Chaolei, Jiazhen, and Ruonan!.
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.
Oct, 2023 Our research group has the 1st group outing activity: Hiking in MacLehose Trail (Section 2).
Oct, 2023 I was ranked as the World’s Top 2% Most-cited Scientists (in the single year 2022) by Stanford University.

Recent Publications

  1. 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
  2. arXiv
    [New!!] Improving Diffusion-based Data Augmentation with Inversion Spherical Interpolation
    Yanghao Wang, and Long Chen
    arXiv preprint (arXiv) , arXiv
  3. 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
  4. 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
  5. arXiv
    [New!!] Compositional Zero-shot Learning via Progressive Language-based Observations
    Lin Li, Guikun Chen, Jun Xiao, and Long Chen
    arXiv preprint (arXiv) , arXiv
  6. 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
  7. 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
  8. 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
  9. ECCV
    [New!!] 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
  10. ECCV
    [New!!] 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. IJCV
    Learning Combinatorial Prompts for Universal Controllable Image Captioning
    Zhen Wang, Jun Xiao, Lei Chen, Fei Gao, Jian Shao, and Long Chen
    International Journal of Computer Vision (IJCV) , 2024
  17. EMNLP Findings
    IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models
    Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad A. Ayyubi, Kai-Wei Chang, and Shih-Fu Chang
    Empirical Methods in Natural Language Processing (EMNLP Findings) , 2023 , Codes
  18. NeurIPS
    Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models
    Lin Li, Jun Xiao, Guikun Chen, Jian Shao, Yueting Zhuang, and Long Chen
    Neural Information Processing Systems (NeurIPS) , 2023 , Codes
  19. ICCV
    Compositional Feature Augmentation for Unbiased Scene Graph Generation
    Lin Li, Guikun Chen, Jun Xiao, Yi Yang, Chunping Wang, and Long Chen
    International Conference on Computer Vision (ICCV) , 2023 , Codes
  20. ACL Findings
    Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs
    Mingyang Zhou, Yi R. Fung, Long Chen, Christopher Thomas, Heng Ji, and Shih-Fu Chang
    Annual Meeting of the Association for Computational Linguistics (ACL Findings) , 2023 , Codes
  21. ICLR
    Video Scene Graph Generation from Single-Frame Weak Supervision
    Siqi Chen, Jun Xiao, and Long Chen
    International Conference on Learning Representations (ICLR) , 2023 , Codes
  22. ICLR
    Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
    Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, and Qianru Sun
    International Conference on Learning Representations (ICLR) , 2023 , Codes
  23. ICLR
    TempCLR: Temporal Alignment Representation with Contrastive Learning
    Yuncong Yang, Jiawei Ma, Shiyuan Huang, Long Chen, Xudong Lin, Guangxing Han, and Shih-Fu Chang
    International Conference on Learning Representations (ICLR) , 2023 , Codes
  24. TPAMI
    Counterfactual Samples Synthesizing and Training for Robust Visual Question Answering
    Long Chen, Yuhang Zheng, Yulei Niu, Hanwang Zhang, and Jun Xiao
    IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 2023 , extension of CVPR’20 work