Multimodal Retrieval Models

Multimodal Retrieval Models is a retrieval framework that integrates and aligns information from multiple modalities—such as text, image, audio, and video—to enable cross-modal or multimodal query and document matching, distinguishing itself from unimodal retrieval models by leveraging heterogeneous data representations within a shared semantic space.

405

Publications

29K

Citations

2K

Authors

420

Institutions

Publications per year

2017–2026

270

Authors

2K

  1. HT
    Heng Tao Shen

    University of Electronic Science and Technology of China, China

    10 publications · 298 citations

    10

    h-index

  2. XX
    Xing Xu

    University of Electronic Science and Technology of China, China

    10 publications · 279 citations

    10

    h-index

  3. CG
    Cathal Gurrin

    Dublin City University, Ireland

    7 publications · 243 citations

    7

    h-index

  4. YP
    Yuxin Peng

    Peking University, China

    7 publications · 504 citations

    7

    h-index

  5. LN
    Liqiang Nie

    Shandong University, China

    7 publications · 561 citations

    7

    h-index

1–5 of 2K

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 3.5K citations

    20

    h-index

  2. IEEE Transactions on Multimedia

    17 publications · 985 citations

    16

    h-index

  3. 15

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 13 publications · 1.2K citations

    13

    h-index

  5. 10

    h-index

1–5