Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts-Reference-Cited by-同舟云学术

Unsupervised Cross-Domain Image Retrieval with Semantic-Attended Mixture-of-Experts

Published:2024-07-10 Issue: Volume:35 Page:197-207
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wang Kai¹^ORCID,Liu Jiayang¹^ORCID,Xu Xing¹^ORCID,Song Jingkuan¹^ORCID,Liu Xin²^ORCID,Shen Heng Tao³^ORCID

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. College of Computer Science and Technology, Huaqiao University, Xiamen, China

3. College of Electronic and Information Engineering, Tongji University, Shanghai, China

Funder

Technology Innovation Committee of Shenzhen Municipality Foundation

New Cornerstone Science Foundation through the XPLORER PRIZE

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657826

Reference55 articles.

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2. Image retrieval by examples

3. Emerging Properties in Self-Supervised Vision Transformers

4. Xiaokang Chen, Mingyu Ding, Xiaodi Wang, Ying Xin, Shentong Mo, Yunhao Wang, Shumin Han, Ping Luo, Gang Zeng, and Jingdong Wang. 2023. Context autoencoder for self-supervised representation learning. International Journal of Computer Vision (2023), 1--16.

5. On the representation collapse of sparse mixture of experts;Chi Zewen;Advances in Neural Information Processing Systems,2022