Ji Zhang

I'm currently a Ph.D student in the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), where I'm very fortunate to be advised by Prof. Jingkuan Song and Prof. Lianli Gao.

My research mainly focuses on Few-shot Learning (FSL), Open-world Learning, and Multimodel Learning. I'm now especially interested in developing advanced algorithms that enable rapid adaptation of large pre-trained vision and language models, e.g. via Prompt Tuning, In-context Learning. If you are also interested in related topics, please do not hesitate to reach out.

Scholar / Github / Email / WeChat

News

[02/2024] One paper about Prompt Tuning is accepted by CVPR'24.
[10/2023] One paper about OOD Detection is accepted by IEEE TIP.
[10/2023] I'm awarded the "Graduate Chinese National Scholarship".
[07/2023] One paper about FSL is accepted by ICCV'23.
[04/2023] One paper about FSL is accepted by ICML'23.
[06/2022] Two papers about FSL and Continue Learning are accepted by ACM MM'22.
[03/2022] One paper about FSL is accepted by IEEE TCSVT.
[06/2021] One paper about FSL is accepted by ACM MM'21.

Selected Papers

	DePT: Decoupled Prompt Tuning Ji Zhang, Shihan Wu, Lianli Gao, Hengtao Shen, Jingkuan Song IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 [Paper][Code] Overcoming the base-new tradeofff (BNT) problem for existing prompt tuning methods.
	From Global to Local: Multi-scale Out-of-distribution Detection Ji Zhang, Lianli Gao, Bingguang Hao, Hao Huang, Jingkuan Song, Hengtao Shen IEEE Transactions on Image Procesing (TIP), 2023 [Paper][Code] Leveraging both global visual information and local region details of images to maximally benefit OOD detection.
	DETA: Denoised Task Adaptation for Few-shot Learning Ji Zhang, Lianli Gao, Xu Luo, Hengtao Shen, Jingkuan Song IEEE International Conference on Computer Vision (ICCV), 2023 [Paper][Code] Tacking both the X-noise (i.e., image noise) and the Y-noise (i.e., label noise) in a unified framework for test-time few-shot tasks.
	A Closer Look at Few-shot Classification Again Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, Jingkuan Song International Conference on Machine Learning (ICML), 2023 [Paper][Code] Empirically proving the disentanglement of training and test-time adaptation algorithms in few-shot classification.
	Free-lunch for Cross-domain Few-shot learning: Style-aware Episodic Training with Robust Contrastive Learning Ji Zhang, Jingkuan Song, Lianli Gao, Hengtao Shen ACM International Conference on Multimedia (ACM MM), 2022 [Paper][Code] Addressing the side-effect of style-shift between tasks from source and target domains.
	Class Gradient Projection for Continual Learning Cheng Chen, Ji Zhang, Jingkuan Song, Lianli Gao ACM International Conference on Multimedia (ACM MM), 2022 [Paper][Code] Projecting the gradient update orthogonal to the gradient subspace of individual classes to mitigate the catastrophic forgetting issue in continual learning.
	Progressive Meta-learning with Curriculum Ji Zhang, Jingkuan Song, Lianli Gao, Ye Liu, Heng Tao Shen IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2022 [Paper][Code] An extended version the ACM MM'21 paper, where the curriculum is effectively integrated as a regularization term into the objective so that the meta-learner can measure the hardness of tasks adaptively.
	Curriculum-based Meta-learning Ji Zhang, Jingkuan Song, Yazhou Yao, Lianli Gao ACM International Conference on Multimedia (ACM MM), 2021 [Paper][Code] Progressively improving the meta-learner by performing episodic training on simulating tasks from easy to hard, i.e., in a curriculum learning manner.

Academic Service

I'm a reviewer of several top journals/conferences, e.g. CVPR, ECCV, ACM MM, AAAI, IEEE TIP, IEEE TMM, IEEE TCSVT.

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