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

Citations

6K

Authors

1.3K

Institutions

Publications per year

2017–2026

1.4K

Authors

6K

  1. WZ
    Wujie Zhou

    Zhejiang University of Science and Technology, China

    15 publications · 1.1K citations

    15

    h-index

  2. JL
    Jingsheng Lei

    Zhejiang University of Science and Technology, China

    12 publications · 903 citations

    12

    h-index

  3. LY
    Lu Yu

    Zhejiang University, China

    9 publications · 650 citations

    9

    h-index

  4. SS
    Shaojing Su

    National University of Defense Technology, China

    6 publications · 255 citations

    6

    h-index

  5. HL
    Huchuan Lu

    Dalian University of Technology, China

    6 publications · 1.2K citations

    6

    h-index

1–5 of 6K

Venues

  1. IEEE Access

    82 publications · 4.6K citations

    36

    h-index

  2. Remote Sensing

    59 publications · 3.3K citations

    33

    h-index

  3. arXiv (Cornell University)

    Repository · 61 publications · 2.5K citations

    31

    h-index

  4. 29

    h-index

  5. Sensors

    44 publications · 2.4K citations

    28

    h-index

1–5