Multimodal Feature Fusion

Multimodal Feature Fusion is a feature-level fusion technique that integrates complementary representations from multiple modalities into a unified feature space through joint transformation and alignment to enhance discriminative capability in multimodal analysis.

2.5K

Publications

164.8K

Citations

9.3K

Authors

1.9K

Institutions

Publications per year

2017–2026

2K

Variants

Multimodal Feature Integration

Authors

9.3K

  1. VD
    Vince D. Calhoun

    Mind Research Network, United States

    19 publications · 1.5K citations

    16

    h-index

  2. HH
    Haifeng Hu

    Sun Yat-sen University, China

    16 publications · 822 citations

    16

    h-index

  3. JH
    Jungong Han

    Lancaster University, United Kingdom

    14 publications · 1.5K citations

    14

    h-index

  4. LM
    Louis‐Philippe Morency

    Carnegie Mellon University, United States

    15 publications · 7K citations

    14

    h-index

  5. QZ
    Qiang Zhang

    Xidian University, China

    13 publications · 1.4K citations

    13

    h-index

1–5 of 9.3K

Venues

  1. Information Fusion

    59 publications · 5.8K citations

    41

    h-index

  2. IEEE Access

    97 publications · 5.2K citations

    40

    h-index

  3. 40

    h-index

  4. IEEE Transactions on Multimedia

    53 publications · 4K citations

    39

    h-index

  5. IEEE Transactions on Image Processing

    42 publications · 4.5K citations

    33

    h-index

1–5