Feature Map Fusion
Feature Map Fusion is a feature-level fusion technique that integrates salient spatial and channel-wise representations from multiple source modalities or layers by aligning and combining their respective feature maps to enhance discriminative capacity in deep learning models.
1.5K
Publications
95.2K
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6K
Authors
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Publications per year
2017–2026
1.4K
Parents
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Dual Attention Network for Scene Segmentation
Jun Fu, Jing Liu, Haijie Tian et al. · 2019 · 6.8K citations
Multi-view 3D Object Detection Network for Autonomous Driving
Xiaozhi Chen, Huimin Ma, Ji Wan et al. · 2017 · 3.4K citations
Yimian Dai, Fabian Gieseke, Stefan Oehmcke et al. · 2021 · 1.1K citations
Omni-Scale Feature Learning for Person Re-Identification
Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro et al. · 2019 · 987 citations
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Chinese Academy of Sciences
Beijing, China
37 h-index
215 publications
49.3K citations
University of Chinese Academy of Sciences
Beijing, China
29 h-index
94 publications
23.2K citations
Wuhan University
Wuhan, China
25 h-index
122 publications
6.6K citations
Tianjin University
Tianjin, China
22 h-index
87 publications
4.1K citations

Zhejiang University
Hangzhou, China
21 h-index
79 publications
3.6K citations
Chongqing University of Posts and Telecommunications
Chongqing, China
21 h-index
68 publications
3.7K citations

Xidian University
Xi'an, China
21 h-index
104 publications
10.4K citations

Northwestern Polytechnical University
Xi'an, China
21 h-index
71 publications
5.3K citations
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82 publications · 4.6K citations
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59 publications · 3.3K citations
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Repository · 61 publications · 2.5K citations
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38 publications · 2.9K citations
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44 publications · 2.4K citations
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