Automatic Multimodal Annotation

Automatic Multimodal Annotation is a computational methodology that systematically assigns linguistic, visual, and paralinguistic metadata to multimodal data through algorithmic inference, distinguishing it from manual or unimodal annotation approaches by its capacity to integrate and process multiple communication channels simultaneously and without human intervention.

115

Publications

5.6K

Citations

490

Authors

172

Institutions

Publications per year

2017–2026

55

Authors

490

  1. LM
    Louis‐Philippe Morency

    Carnegie Mellon University, United States

    4 publications · 851 citations

    4

    h-index

  2. RS
    Radu Soricut

    Google (United States), United States

    3 publications · 78 citations

    3

    h-index

  3. DG
    Daniel Gática-Pérez

    Idiap Research Institute, Switzerland

    2 publications · 31 citations

    2

    h-index

  4. WR
    Wasifur Rahman

    University of Rochester, United States

    2 publications · 748 citations

    2

    h-index

  5. CE
    Clara E. Rivera

    Google (United States), United States

    2 publications · 47 citations

    2

    h-index

1–5 of 490

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 241 citations

    7

    h-index

  2. IEEE Transactions on Multimedia

    3 publications · 103 citations

    3

    h-index

  3. 2

    h-index

  4. Pattern Recognition

    2 publications · 108 citations

    2

    h-index

  5. Visual Studies

    1 publications · 51 citations

    1

    h-index

1–5