Probabilistic Feature Fusion

Probabilistic Feature Fusion is a feature-level fusion method that integrates multimodal features by modeling their uncertainty and interdependencies through probabilistic frameworks to enhance representation learning.

63

Publications

2.6K

Citations

234

Authors

118

Institutions

Publications per year

2017–2026

19

Authors

234

  1. TS
    Thomas S. Huang

    University of Illinois Urbana-Champaign, United States

    2 publications · 35 citations

    2

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  2. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    2 publications · 22 citations

    2

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  3. LZ
    Luming Zhang

    Hefei University of Technology, China

    2 publications · 75 citations

    2

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  4. DA
    Dejan Arsić

    Technical University of Munich, Germany

    2 publications · 22 citations

    2

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  5. GR
    Gerhard Rigoll

    Technical University of Munich, Germany

    2 publications · 22 citations

    2

    h-index

1–5 of 234

Venues

  1. Neurocomputing

    4 publications · 89 citations

    4

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  2. 2

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  3. IEEE Transactions on Multimedia

    2 publications · 57 citations

    2

    h-index

  4. IEEE Access

    1 publications · 25 citations

    1

    h-index

  5. The Visual Computer

    1 publications · 24 citations

    1

    h-index

1–5