
Long Chen
Assistant Professor
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology (HKUST)
Clear Water Bay, Kowloon, Hong Kong
Office: Room 3508 (via Lift 25 & 26), Academic Building
Email: longchen A~T ust.hk
Dr. Long CHEN (Chinese Name: 陈隆) is a tenure-track Assistant Professor in the CSE department, The Hong Kong University of Science and Technology (2023 - present). 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. period, he also worked closely with Prof. Hanwang Zhang from Nanyang Technological University, Prof. Shih-Fu Chang from Columbia University, and Prof. Tat-Seng Chua from National University of Singapore. 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 working with Dr. Wei Liu (2020 - 2021).
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 other AI areas, such as the appearance of Large Language Models (LLMs) in the Natural Language Processing
community, we are also very interested in several releveant cutting-edge directions:
4) Building more explainable, robust, and universal vision models with the help of LLMs.
5) Designing more efficient and stronger multimodal LLMs.
6) The inherent weaknesses in existing LLMs.
News
Jun, 2023 | I will serve as an Area Chair for CVPR 2024 and a Senior PC for AAAI 2024. |
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Mar, 2023 | I will serve as an Area Chair for NeurIPS 2023 and an Area Chair for BMVC 2023. |
Nov, 2022 | I will serve as a Senior PC for IJCAI 2023 and an Action Editor for ACL Rolling Review (ARR). |
Oct, 2022 | I will serve as an Area Chair for CVPR 2023. |
Jul, 2022 | I will serve as an Area Chair for BMVC 2022 and a Senior PC for AAAI 2023. |
Apr, 2022 | My dissertation was reported by the official wechat account of ZJU. |
Nov, 2021 | I won the 2020 Outstanding Doctoral Dissertation Award at Zhejiang Province. |
Oct, 2021 | We won the best paper award of the 2nd HUMA Workshop in ACM Multimedia 2021. |
Sep, 2021 | I won the 2020 Outstanding Doctoral Dissertation Award at Zhejiang University (Only 1 recipient in ZJU EE/CS). |
Jul, 2021 | We won the 1st place in the 3rd Video Relation Understanding Challenge. The report and codes are released. |
Recent Publications
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arXivarXiv preprint (arXiv) , arXiv , Codes
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arXivarXiv preprint (arXiv) , arXiv
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arXivarXiv preprint (arXiv) , arXiv , Codes
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NeurIPSNeural Information Processing Systems (NeurIPS) , 2023
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ICCVInternational Conference on Computer Vision (ICCV) , 2023 , Codes
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ACL FindingsAnnual Meeting of the Association for Computational Linguistics (ACL Findings) , 2023 , Codes
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ICLRInternational Conference on Learning Representations (ICLR) , 2023 , Codes
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ICLRInternational Conference on Learning Representations (ICLR) , 2023 , Codes
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ICLRInternational Conference on Learning Representations (ICLR) , 2023 , Codes
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TPAMIIEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) , 2023 , extension of CVPR’20 work