I am currently a Full Professor at Hefei University of Technology.

I received my Ph.D. degree from The Chinese University of Hong Kong (CUHK) in 2022, under the supervision of Prof. Jiaya Jia and Prof. Bei Yu. Prior to that, I obtained my B.E. degree in Computer Science from Shandong University in 2018. I also spent two wonderful years at Nanyang Technological University.

Research

Data-centric AI opens the door to Artificial General Intelligence (AGI). However, data biases inevitably exist in large-scale data, like data imbalance, spurious correlations among classes, and annotation noise. I’m interested in fundamental research of model generalization and robustness on different data distributions, realizing effective usage of large-scale data and that the trained models do not suffer from data biases. It usually involves problems in machine learning and computer vision, like distribution shifts, adversarial robustness, fairness, and out-of-distribution generalization.

Additionally, I aim to understand the behaviors of large models, such as Stable Diffusion models, LLMs, and VLMs, and their applications in downstream tasks.

I am excited to explore new machine learning problems, including AI for science and 3D modeling challenges.

Publications [Google Scholar]

* represents equal contributions

• Decoupled Kullback-Leibler Divergence Loss
  Jiequan Cui, Zhuotao Tian, Zhisheng Zhong, Xiaojuan Qi, Bei Yu, Hanwang Zhang.
  Conference on Neural Information Processing Systems (NeurIPS), 2024.
  paper code

• Classes Are Not Equal: An Empirical Study on Image Recognition Fairness
  Jiequan Cui, Beier Zhu, Xin Wen, Xiaojuan Qi, Bei Yu, Hanwang Zhang.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  paper code

• Genralized Parametric Contrastive Learning
  Jiequan Cui, Zhisheng Zhong, Zhuotao Tian, Shu Liu, Bei Yu, Jiaya Jia.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023.
  paper code

• Understanding Imbalanced Semantic Segmentation Through Neural Collapse
  Zhisheng Zhong*, Jiequan Cui*, Yibo Yang*, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, Jiaya Jia.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  paper

• ResLT: Residual Learning for Long-tailed Recognition
  Jiequan Cui, Shu Liu, Zhuotao Tian, Zhisheng Zhong, Jiaya Jia.
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
  paper code

• Parametric Contrastive Learning
  Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, Jiaya Jia.
  IEEE International Conference on Computer Vision (ICCV), 2021.
  paper code

• Learnable Boundary Guided Adversarial Training
  Jiequan Cui, Shu Liu, Liwei Wang, Jiaya Jia.
  IEEE International Conference on Computer Vision (ICCV), 2021.
  paper code

• Fast and Practical Neural Architecture Search
  Jiequan Cui, Pengguang Chen, Ruiyu Li, Shu Liu, Xiaoyong Shen, Jiaya Jia.
  IEEE International Conference on Computer Vision (ICCV), 2019.
  paper code

• Project-Probe-Aggregate: Efficient Fine-Tuning for Group Robustness
  Beier Zhu, Jiequan Cui, Hanwang Zhang, Chi Zhang. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  paper

• CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
  Yuan Zhou, Qingshan Xu, Jiequan Cui, Junbao Zhou, Jing Zhang, Richang Hong, Hanwang Zhang.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2025.
  paper

• Robust Fine-tuning of Zero-shot Models via Variance Reduction
  Beier Zhu, Jiequan Cui, Hanwang Zhang.
  Conference on Neural Information Processing Systems (NeurIPS), 2024.
  paper

• Typicalness-Aware Learning for Failure Detection
  Yijun Liu, Jiequan Cui, Zhuotao Tian, Senqiao Yang, Qingdong He, Xiaoling Wang, Jingyong Su.
  Conference on Neural Information Processing Systems (NeurIPS), 2024.
  paper

• Instruction Tuning-free Visual Token Complement for Multimodal LLMs
  Dongsheng Wang, Jiequan Cui, Miaoge Li, Wang Lin, Bo Chen, Hanwang Zhang.
  European Conference on Computer Vision (ECCV), 2024.
  paper

• Doubly Abductive Counterfactual Inference for Text-based Image Editing
  Xue Song, Jiequan Cui, Hanwang Zhang, Jingjing Chen, Richang Hong, YuGang Jiang.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
  paper code

• Learning Context-aware Classifier for Semantic Segmentation
  Zhuotao Tian, Jiequan Cui, Li Jiang, Xiaojuan Qi, Xin Lai, Yixin Chen, Shu Liu, Jiaya Jia.
  AAAI Conference on Artificial Intelligence (AAAI), 2023.
  paper

• Improving Calibration for Long-Tailed Recognition
  Zhisheng Zhong, Jiequan Cui, Shu Liu, Jiaya Jia.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  paper code

Experiences

• SmartMore, ShenZhen, China.
  Research Intern
  Mentor: Shu Liu
  Time: 2020.03 - 2021.12

• YouTu X-Lab, Tencent, ShenZhen, China.
  Research Intern
  Mentor: Xiaoyong Shen
  Time: 2018.09 - 2020.02

• Reviewer for Journals
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
  International Journal of Computer Vision (IJCV).
  IEEE Transactions on Image Processing (TIP).
  IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

• Reviewer for Conferences
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
  IEEE International Conference on Computer Vision (ICCV).
  European Conference on Computer Vision (ECCV).
  International Conference on Learning Representations (ICLR).
  Neural Information Processing Systems (NeurIPS).
  International Conference on Machine Learning(ICML).