Multimodal Information Extraction

Multimodal Information Extraction is a computational process that identifies and structurally represents salient, interconnected data elements from multiple heterogeneous modalities—such as text, image, audio, and video—by integrating cross-modal signals to produce unified, semantically coherent information structures.

260

Publications

14K

Citations

1.1K

Authors

359

Institutions

Publications per year

2017–2026

179

Authors

1.1K

  1. RR
    Rajiv Ratn Shah

    Indraprastha Institute of Information Technology Delhi, India

    6 publications · 237 citations

    6

    h-index

  2. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    5 publications · 227 citations

    5

    h-index

  3. RZ
    Roger Zimmermann

    National University of Singapore, Singapore

    4 publications · 146 citations

    4

    h-index

  4. TN
    Tomoaki Nakamura

    University of Electro-Communications, Japan

    3 publications · 134 citations

    3

    h-index

  5. XH
    Xiaodong He

    Microsoft (United States), United States

    3 publications · 117 citations

    3

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 388 citations

    10

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 9 publications · 853 citations

    9

    h-index

  3. IEEE Transactions on Multimedia

    8 publications · 853 citations

    8

    h-index

  4. 6

    h-index

  5. Proceedings of the 30th ACM International Conference on Multimedia

    Conference · 5 publications · 194 citations

    5

    h-index

1–5