Da-Wei Zhou - Homepage

Short Bio

I am a final-year Ph.D. candidate in the LAMDA Group, Nanjing University (NJU), advised by Prof. De-Chuan Zhan and Han-Jia Ye. Prior to attending NJU, I obtained my B.Sc. degree from Hunan University in June 2018. From October 2022 to October 2023, I had the invaluable experience of being a visiting scholar at MMLab@NTU, working closely with Prof. Ziwei Liu.

My research interests include machine learning and deep learning, currently focusing on class-incremental learning and reusing pre-trained 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

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

[2024-01] One survey about pre-trained model-based continual learning is uploaded to arXiv.

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

[2023-09] We have released PILOT toolbox for class-incremental learning with pre-trained models (technical report).

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

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

[2023-05] One paper about class-incremental learning with VLM is uploaded to arXiv.

[2023-03] One paper about class-incremental learning with PTM is uploaded to arXiv.

[2023-02] One survey about class-incremental learning is uploaded to arXiv.

[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.

[2021-12] One paper about active incremental learning is accepted to TKDE.

[2021-08] One paper about open-world learning is accepted to TNNLS.

[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

Continual Learning with Pre-Trained Models: A Survey

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

arXiv:2401.16386. 2024.
[arXiv] [Code] [Media] [中文解读]
Preprint

Learning without Forgetting for Vision-Language Models

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

arXiv:2305.19270. 2023.
[arXiv]
Preprint

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

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

arXiv:2303.07338. 2023.
[arXiv] [Code]
Preprint

Deep Class-Incremental Learning: A Survey

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

arXiv:2302.03648. 2023.
[arXiv] [Code] [Media] [中文解读]

Conference Paper

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]
ICLR

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

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

Eleventh 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

Eleventh 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

Thirty-seventh Conference on 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] [Project Page] [Code]
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

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] [Project Page] [Code]
ACM MM

Co-Transport for Class-Incremental Learning

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

The 29th ACM International Conference on Multimedia. ACM MM 2021.
[Paper] [Project Page] [Code]

Journal Article

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. ESI Highly Cited Paper
[Paper] [Code]
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.
[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.
[Paper] [Code] [Media] [中文解读]

Tutorials

Continual Learning and Its Extension to Pre-trained Models (IJCAI 2023)
With Prof. Ying Wei and Prof. Han-Jia Ye
Slids: [Part1] [Part2]

Many thanks to Yaoyao for sharing this great theme.