Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval-Reference-Cited by-同舟云学术

Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval

Author:

Wen Haokun¹^ORCID,Song Xuemeng²^ORCID,Yin Jianhua¹^ORCID,Wu Jianlong²^ORCID,Guan Weili³^ORCID,Nie Liqiang¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China

2. School of Computer Science and Technology, Shandong University, Tsingtao, China

3. Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Cited by 4 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. Fine-grained Textual Inversion Network for Zero-Shot Composed Image Retrieval;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

2. Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

3. Self-Distilled Dynamic Fusion Network for Language-Based Fashion Retrieval;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14