Attention-based Feature Interactions
Attention-Based Feature Interactions is a machine learning method that selectively emphasizes relevant feature combinations through attention mechanisms, distinguishing itself from other feature interaction approaches by dynamically weighting the importance of interacting features based on contextual input.
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4.2K
Citations
233
Authors
110
Institutions
Publications per year
2017–2026
49
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Bilinear Attention Networks for Person Retrieval
Pengfei Fang, Jieming Zhou, Soumava Kumar Roy et al. · 2019 · 174 citations
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Hefei University of Technology
Hefei, China
3 h-index
8 publications
317 citations

Monash University
Melbourne, Australia
2 h-index
2 publications
238 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
2 publications
154 citations

Zhejiang University
Hangzhou, China
2 h-index
4 publications
2.6K citations

Chinese Academy of Sciences
Beijing, China
2 h-index
5 publications
213 citations
University College London
London, United Kingdom
2 h-index
4 publications
287 citations

National University of Defense Technology
Changsha, China
2 h-index
5 publications
126 citations

Beihang University
Beijing, China
2 h-index
6 publications
572 citations
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Repository · 7 publications · 377 citations
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Conference · 4 publications · 368 citations
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2 publications · 39 citations
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TKDE · 2 publications · 88 citations
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2 publications · 40 citations
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