Da-Wei Zhou - Homepage

Short Bio

I am an Associate Researcher (2024-) in the School of Artificial Intelligence at Nanjing University. I am a member of LAMDA Group. Previously, I received my Ph.D. (2018-2024) from Nanjing University, advised by Prof. De-Chuan Zhan and Prof. Han-Jia Ye. I was fortunate to visit MMLab@NTU (2022-2023), working closely with Prof. Ziwei Liu.

My research interests include machine learning and its applications in computer vision, currently focusing on continual learning, pre-trained model reuse, and multimodal large language models.

I am looking for highly self-motivated students. Please drop me an email with your resume and transcript if you are interested in working together with me.

News

[2025-05] Two papers are accepted to ICML 2025.

[2025-04] One survey about model reuse is accepted to IJCAI 2025.

[2025-03] One paper about class-incremental learning with CLIP is uploaded to arXiv.

[2025-02] Two papers about continual learning are accepted to CVPR 2025.

[2025-02] One paper about class-incremental learning with VLM is accepted to TPAMI.

[2025-01] Our toolbox for class-incremental learning with PTMs (PILOT) is accepted to SCIS.

[2024-12] One paper about class-incremental learning is accepted to AAAI 2025.

[2024-11] Recognized as NeurIPS 2024 Top Reviewer.

[2024-10] We are hosting a tutorial on model reuse at AAAI 2025.

[2024-09] One paper about class-incremental learning with PTM is accepted to MLJ.

[2024-08] One paper about class-incremental learning with PTM is accepted to IJCV.

[2024-07] One survey about class-incremental learning is accepted to TPAMI.

[2024-05] Successfully defended my Ph.D. dissertation.

[2024-05] Recognized as CVPR 2024 Outstanding Reviewer.

[2024-05] One paper about class-incremental learning is accepted to ICML 2024.

[2024-04] One survey about PTM-based continual learning is accepted to IJCAI 2024.

[2024-02] One paper about class-incremental learning is accepted to CVPR 2024.

[2023-11] Recognized as NeurIPS 2023 Top Reviewer.

[2023-09] One paper about few-shot class-incremental learning is accepted to NeurIPS 2023.

[2023-09] One paper about contextualized meta-learning is accepted to TPAMI.

[2023-08] We are hosting the tutorial (slides) on continual learning at IJCAI 2023.

[2023-01] Two papers about class-incremental learning are accepted to ICLR 2023 (one spotlight).

[2022-10] Our toolbox for class-incremental learning (PyCIL) is accepted to SCIS.

[2022-08] One paper about few-shot class-incremental learning is accepted to TPAMI.

[2022-07] One paper about class-incremental learning is accepted to ECCV 2022.

[2022-03] One paper about few-shot class-incremental learning is accepted to CVPR 2022.

[2021-07] One paper about class-incremental learning is accepted to ACM MM 2021.

[2021-03] One oral paper about open-set recognition is accepted to CVPR 2021.

Selected Publications

For more details, please view the full publication page or Google Scholar profile.

Preprints

Preprint

External Knowledge Injection for CLIP-Based Class-Incremental Learning

Da-Wei Zhou, Kai-Wen Li, Jingyi Ning, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan

arXiv 2025
[Paper] [Code]

Conference Paper

CVPR

Dual Consolidation for Pre-Trained Model-Based Domain-Incremental Learning

Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, Lijun Zhang, De-Chuan Zhan

The IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2025
[Paper] [Code] [Media]
CVPR

Task-Agnostic Guided Feature Expansion for Class-Incremental Learning

Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

The IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2025
[Paper] [Code]
ICML

Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts

Lan Li, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

International Conference on Machine Learning. ICML 2025
[Paper] [Code]
ICML

Parrot: Multilingual Visual Instruction Tuning

Hai-Long Sun, Da-Wei Zhou, Yang Li, Shiyin Lu, Chao Yi, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye

International Conference on Machine Learning. ICML 2025
[Paper] [Code]
IJCAI

A Unifying Perspective on Model Reuse: From Small to Large Pre-Trained Models

Da-Wei Zhou, Han-Jia Ye

International Joint Conference on Artificial Intelligence. IJCAI 2025
[Paper] [Project Page]
CVPR

Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning

Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, De-Chuan Zhan

The IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR 2024
[Paper] [Code]
IJCAI

Continual Learning with Pre-Trained Models: A Survey

Da-Wei Zhou, Hai-Long Sun, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan

International Joint Conference on Artificial Intelligence. IJCAI 2024
[Paper] [Code] [Media]
ICML

Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning

Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

International Conference on Machine Learning. ICML 2024
[Paper] [Code]
ICLR Spotlight

A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, De-Chuan Zhan

International Conference on Learning Representations. ICLR 2023 Spotlight Presentation
[Paper] [Code]
ICLR

BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion

Fu-Yun Wang, Da-Wei Zhou, Liu Liu, Yatao Bian, Han-Jia Ye, De-Chuan Zhan, Peilin Zhao

International Conference on Learning Representations. ICLR 2023
[Paper] [Code]
NeurIPS

Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration

Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang, De-Chuan Zhan, Han-Jia Ye

Neural Information Processing Systems. NeurIPS 2023
[Paper] [Code]
CVPR

Forward Compatible Few-Shot Class-Incremental Learning

Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, De-Chuan Zhan

IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2022
[Paper] [Code] [Project Page]
ECCV

FOSTER: Feature Boosting and Compression for Class-Incremental Learning

Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

European Conference on Computer Vision. ECCV 2022
[Paper] [Code]
CVPR Oral

Learning Placeholders for Open-Set Recognition

Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2021 Oral Presentation
[Paper] [Code] [Project Page]
ACM MM

Co-Transport for Class-Incremental Learning

Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan

ACM International Conference on Multimedia. ACM MM 2021
[Paper] [Code] [Project Page]

Journal Article

TPAMI

Learning without Forgetting for Vision-Language Models

Da-Wei Zhou, Yuanhan Zhang, Yan Wang, Jingyi Ning, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI 2025
[Paper] [Code] [Media]
TPAMI

Class-Incremental Learning: A Survey

Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI 2024 TPAMI Popular Documents
[Paper] [Code] [Media]
TPAMI

Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, De-Chuan Zhan

IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI 2023 ESI Highly Cited Paper
[Paper] [Code]
IJCV

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

Da-Wei Zhou, Zi-Wen Cai, Han-Jia Ye, De-Chuan Zhan, Ziwei Liu

International Journal of Computer Vision. IJCV 2025
[Paper] [Code] [Media]
TPAMI

Contextualizing Meta-Learning via Learning to Decompose

Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan

IEEE Transactions on Pattern Analysis and Machine Intelligence. TPAMI 2024
[Paper] [Code]
SCIS

PyCIL: A Python Toolbox for Class-Incremental Learning

Da-Wei Zhou*, Fu-Yun Wang*, Han-Jia Ye, De-Chuan Zhan

SCIENCE CHINA Information Sciences. SCIS 2023
[Paper] [Code] [Media] [中文解读]

Contact

Office: A202, Yifu Building
Email: zhoudw (at) lamda.nju.edu.cn or zhoudw (at) nju.edu.cn

Many thanks to Yaoyao for sharing this great theme.