Cui Jiequan
Date of Birth:1995-09-20
Gender:Male
Education Level:Postgraduate (Doctoral)
Alma Mater:The Chinese University of Hong Kong
Scientific Research
Research Field
Data-centric AI plays a crucial role in advancing towards Artificial General Intelligence (AGI). My research focuses on developing novel algorithms and theoretical foundations — such as contrastive learning and generative learning — to more effectively leverage data for enhancing model generalization and robustness.
In addition, my team actively explores multi-modal alignment and robustness in large models, including LLMs and VLMs. Our work addresses critical challenges such as jailbreak attacks, adversarial robustness, and hallucination mitigation.
I am also broadly interested in emerging machine learning topics, including AI for science and 3D modeling, and always enthusiastic about tackling new challenges in these domains.
Paper Publications
- Jiequan Cui,Shu Liu,Liwei Wang,Jiaya Jia,Learnable Boundary Guided Adversarial Training:ICCV 2021
- Jiequan Cui,Zhisheng Zhong,Shu Liu,Bei Yu,Jiaya Jia,Parametric Contrastive Learning:ICCV 2021
- Jiequan Cui,Shu Liu,Zhuotao Tian,Zhisheng Zhong,Jiaya Jia,ResLT: Residual Learning for Long-tailed Recognition:TPAMI 2022
- Jiequan Cui,Zhisheng Zhong,Zhuotao Tian,Shu Liu,Bei Yu,Jiaya Jia,Genralized Parametric Contrastive Learning:TPAMI 2023
- Jiequan Cui,Beier Zhu,Xin Wen,Xiaojuan Qi,Bei Yu,Hanwang Zhang,Classes Are Not Equal: An Empirical Study on Image Recognition Fairness:CVPR 2024
Patents
- No content
Published Books
- No content
Research Projects
- No content