Universal Domain Adaptation via Compressive Attention Matching

Author:

Zhu Didi1,Li Yinchuan2,Yuan Junkun1,Li Zexi1,Kuang Kun1,Wu Chao1

Affiliation:

1. Zhejiang University

2. Huawei Noah’s Ark Lab

Funder

Research and Development

National Natural Science Foundation of China

Zhejiang University

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Neural Collapse Anchored Prompt Tuning for Generalizable Vision-Language Models;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

2. Vision transformers in domain adaptation and domain generalization: a study of robustness;Neural Computing and Applications;2024-08-22

3. Intelligent Model Update Strategy for Sequential Recommendation;Proceedings of the ACM Web Conference 2024;2024-05-13

4. Stochastic Binary Network for Universal Domain Adaptation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

5. ROSE: Relational and Prototypical Structure Learning for Universal Domain Adaptive Hashing;IEEE Transactions on Information Forensics and Security;2024

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