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Postdoctoral Researcher [Google Scholar] [Github] 3/F, 17W, Science Park West Avenue, Hong Kong Science Park, Hong Kong. |
About me
I am currently a postdoctoral researcher at the Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, working with Prof. Zhaoxiang Zhang. I received my Ph.D. in Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy of Sciences, where I was advised by Prof. Cheng-Lin Liu and Prof. Xu-Yao Zhang. Prior to this, I received the B.E. degree from Tsinghua University.
Research Highlights: My research interests include topics in reliability (e.g., uncertainty estimation and calibration, failure detection) and adaptability (e.g., continual learning, novel class discovery) of mechine learning models (especially foundation models such as LVMs, LLMs and MLMs）in the open world/environment applications.
I am actively seeking highly motivated students. If you are interested, please send me an email with your CV.
Preprints
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning. ArXiv 2024 [paper].
Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu.
Open-world machine learning: A review and new outlooks. ArXiv 2024 [paper].
Fei Zhu, Shijie Ma, Zhen Cheng, Xu-Yao Zhang, Zhaoxiang Zhang, Cheng-Lin Liu.
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning. ArXiv 2024 [paper].
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu.
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning. ArXiv 2024 [paper].
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu.
Multi-scale Unified Network for Image Classification. ArXiv 2024 [paper].
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu.
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning. ArXiv 2025 [paper].
Haiyang Guo, Fei Zhu, Fan-hu Zeng, Bing Liu, Xu-Yao Zhang.
Dual-Modality Guided Prompt for Continual Learning of Large Multimodal Models. ArXiv 2025 [paper].
Fan-hu Zeng, Fei Zhu, Haiyang Guo, Xu-Yao Zhang, Cheng-Lin Liu.
Selected Publications
[ICLR 2025] C-CLIP: Multimodal Continual Learning for Vision-Language Model [paper].
Wen-Zhuo Liu, Fei Zhu📧, Longhui Wei, Qi Tian.
[TNNLS 2025] Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection [paper].
Zhen Cheng, Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu.
[NeurIPS 2024] MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution [paper].
Wenzhuo Liu, Fei Zhu, Shijie Ma, Cheng-Lin Liu.
[NeurIPS 2024] Happy: A Debiased Learning Framework for Continual Generalized Category Discovery [paper] [code].
Shijie Ma, Fei Zhu, Zhun Zhong, Xu-Yao Zhang, Cheng-Lin Liu.
[IJCV 2024] Breaking the Limits of Reliable Prediction via Generated Data [paper].
Zhen Cheng, Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu.
[PR 2024] Towards trustworthy dataset distillation [paper] [code].
Shijie Ma, Fei Zhu, Zhen Cheng, Xu-Yao Zhang.
[ECCV 2024] PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning [paper] [arxiv] [code].
Haiyang Guo, Fei Zhu, Wenzhuo Liu, Xu-Yao Zhang, Cheng-Lin Liu.
[CVPR 2024] RCL: Reliable Continual Learning for Unified Failure Detection [paper] [code].
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu, Zhaoxiang Zhang.
[CVPR 2024] Active Generalized Category Discovery [paper] [arxiv] [code].
Shijie Ma, Fei Zhu, Zhun Zhong, Xu-Yao Zhang, Cheng-Lin Liu.
[TPAMI 2024] Revisiting Confidence Estimation: Towards Reliable Failure Prediction [paper] [arxiv] [code].
Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu.
[TPAMI 2023] Learning by Seeing More Classes [paper].
Fei Zhu, Xu-Yao Zhang, Rui-Qi Wang, Cheng-Lin Liu.
[Neural Networks 2023] Imitating the Oracle: Towards Calibrated Model for Class Incremental Learning [paper] [code].
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu.
[CVPR 2023 Highlight Paper (Top 2.5%)] OpenMix: Exploring Outlier Samples for Misclassification Detection [paper] [arxiv] [code].
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu.
[ECCV 2022] Rethinking Confidence Calibration for Failure Prediction [paper] [arxiv] [code].
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu.
[CVPR 2021 Oral Paper (Top 4%)] Prototype Augmentation and Self-Supervision for Incremental Learning [paper] [code].
Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, Cheng-Lin Liu.
[NeurIPS 2021] Class-Incremental Learning via Dual Augmentation [paper] [code].
Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu.
[IEEE/CAA JAS 2023 Invited Reviews] Class Incremental Learning: A Review and Performance Evaluation (In Chinese) [paper].
Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu.
[PR 2023] Adversarial Training with Distribution Normalization and Margin Balance [paper].
Zhen Cheng, Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu.
[Nature Communications 2022 Highlight Paper] Decoding lip language using triboelectric sensors with deep learning [paper].
Yi-Jia Lu*, Han Tan*, Jia Cheng*, Fei Zhu, Bing Liu, Shan-Shan Wei, LinHong Ji, Zhong-Lin Wang.
Academic Services
Conference Reviewer: NeurIPS, ICLR, ICML, CVPR, ICCV, ECCV
Journal Reviewer: IEEE TIP, TNNLS, TMM, TKDE, PR, NN, IJCV
Workshop Organizer: Trustworthy Model and Learning in Open Environment, PRCV 2024
Talk
Unknown Rejection in Open Environment, Biomedical Engineering Distinguished Lecture Series, Southern University of Science and Technology, August, 2024
Deep Continual Learning, School of Computer Science and Engineering, Nanjing University of Science and Technology, January, 2025
Open-Environment Continual Learning, Forum of Zhongguancun College, Beijing, February, 2025