Joint Feature Embedding
Joint Feature Embedding is a multimodal fusion method that learns a shared latent representation space by simultaneously integrating features from multiple modalities through coordinated transformations, thereby preserving cross-modal correlations while enabling unified downstream processing.
539
Publications
37.6K
Citations
2K
Authors
591
Institutions
Publications per year
2017–2026
464
Parents
539
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
De Cheng, Yihong Gong, Sanping Zhou et al. · 2016 · 1.3K citations
Joint Discriminative and Generative Learning for Person Re-Identification
Zhedong Zheng, Xiaodong Yang, Zhiding Yu et al. · 2019 · 859 citations
Learning Deep Structure-Preserving Image-Text Embeddings
Liwei Wang, Yin Li, Svetlana Lazebnik · 2016 · 808 citations
1–5 of 539
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University of North Carolina at Chapel Hill, United States
5 publications · 1.7K citations
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5
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591

Chinese Academy of Sciences
Beijing, China
22 h-index
97 publications
4.5K citations
University of Chinese Academy of Sciences
Beijing, China
17 h-index
50 publications
2K citations
Tsinghua University
Beijing, China
14 h-index
32 publications
1.4K citations
University of Technology Sydney
Sydney, Australia
13 h-index
27 publications
6.2K citations

Hefei University of Technology
Hefei, China
13 h-index
29 publications
1.6K citations

Xidian University
Xi'an, China
13 h-index
30 publications
2.4K citations

Peking University
Beijing, China
12 h-index
29 publications
2.1K citations
Wuhan University
Wuhan, China
12 h-index
36 publications
1.4K citations
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Repository · 55 publications · 2.2K citations
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31 publications · 1.4K citations
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22 publications · 550 citations
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15 publications · 995 citations
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16 publications · 1K citations
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