Multi-modal Retrieval
Multi-Modal Retrieval is a retrieval paradigm that enables the joint processing and alignment of information from two or more distinct modalities—such as text, image, audio, and video—to achieve content-based querying and matching across heterogeneous data sources.
686
Publications
38.6K
Citations
2K
Authors
515
Institutions
Publications per year
2017–2026
465
Parents
686
Adversarial Cross-Modal Retrieval
Bokun Wang, Yang Yang, Xing Xu et al. · 2017 · 772 citations · Full text
Collective Matrix Factorization Hashing for Multimodal Data
Guiguang Ding, Yuchen Guo, Jile Zhou · 2014 · 694 citations
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
Chao Li, Cheng Deng, Ning Li et al. · 2018 · 479 citations
1–5 of 686
2K
15
h-index
15
h-index
15
h-index
University of Electronic Science and Technology of China, China
14 publications · 2K citations
14
h-index
University of Electronic Science and Technology of China, China
14 publications · 1.7K citations
14
h-index
1–5 of 2K
515

University of Electronic Science and Technology of China
Chengdu, China
25 h-index
83 publications
8.4K citations

Chinese Academy of Sciences
Beijing, China
25 h-index
161 publications
10.7K citations

Shandong Normal University
Jinan, China
21 h-index
83 publications
3.7K citations
University of Chinese Academy of Sciences
Beijing, China
21 h-index
53 publications
3.1K citations
Tsinghua University
Beijing, China
20 h-index
62 publications
6.8K citations

Zhejiang University
Hangzhou, China
19 h-index
71 publications
5.8K citations

Peking University
Beijing, China
18 h-index
48 publications
3.8K citations
National University of Singapore
Singapore, Singapore
18 h-index
42 publications
4.4K citations
1–8 of 515
34 publications · 2.2K citations
27
h-index
Conference · 22 publications · 2.3K citations
20
h-index
22 publications · 2.4K citations
20
h-index
Repository · 37 publications · 2.3K citations
18
h-index
14 publications · 830 citations
15
h-index
1–5