Attention-based Matching
Attention-Based Matching is a message matching method that employs attention mechanisms to dynamically align and weigh relevant parts of messages by focusing on contextually significant features, thereby enhancing correspondence accuracy through adaptive relevance scoring.
104
Publications
7.5K
Citations
381
Authors
121
Institutions
Publications per year
2017–2026
93
Parents
104
Dual Attention Networks for Multimodal Reasoning and Matching
Hyeonseob Nam, Jung-Woo Ha, Jeonghee Kim · 2017 · 747 citations
Gated Self-Matching Networks for Reading Comprehension and Question Answering
Wenhui Wang, Nan Yang, Furu Wei et al. · 2017 · 721 citations · Full text
Pose-Guided Feature Alignment for Occluded Person Re-Identification
Jiaxu Miao, Yu Wu, Ping Liu et al. · 2019 · 642 citations · Full text
IMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Hui Chen, Guiguang Ding, Xudong Liu et al. · 2020 · 419 citations
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121

Chinese Academy of Sciences
Beijing, China
9 h-index
19 publications
868 citations
Tianjin University
Tianjin, China
7 h-index
20 publications
950 citations
University of Science and Technology of China
Hefei, China
7 h-index
21 publications
976 citations
University of Chinese Academy of Sciences
Beijing, China
7 h-index
15 publications
1.1K citations
Tencent (China)
Shenzhen, China
7 h-index
28 publications
2.3K citations

Xi'an Jiaotong University
Xi'an, China
4 h-index
14 publications
643 citations
Institute of Automation
Beijing, China
4 h-index
12 publications
417 citations
Tsinghua University
Beijing, China
4 h-index
8 publications
1.1K citations
1–8 of 121
Repository · 16 publications · 778 citations
12
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8 publications · 390 citations
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5 publications · 167 citations
5
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3 publications · 195 citations
3
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TOMCCAP · 3 publications · 54 citations
3
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