Vision Transformers

Vision Transformer is a deep learning model that adapts the transformer architecture, originally designed for natural language processing, to image classification by treating image patches as sequential tokens and leveraging self-attention mechanisms to capture global spatial dependencies.

2K

Publications

209.1K

Citations

6.2K

Authors

1.4K

Institutions

Publications per year

2017–2026

2K

Authors

6.2K

  1. JF
    Jiashi Feng

    National University of Singapore, Singapore

    17 publications · 5K citations

    16

    h-index

  2. DT
    Dacheng Tao

    The University of Sydney, Australia

    18 publications · 1.8K citations

    15

    h-index

  3. LY
    Lu Yuan

    Microsoft Research (United Kingdom), United Kingdom

    13 publications · 2.1K citations

    13

    h-index

  4. XZ
    Xiaohua Zhai

    Google (United States), United States

    14 publications · 25.4K citations

    13

    h-index

  5. XW
    Xinggang Wang

    Huazhong University of Science and Technology, China

    10 publications · 1.1K citations

    12

    h-index

1–5 of 6.2K

Venues

  1. arXiv (Cornell University)

    Repository · 373 publications · 45.6K citations

    78

    h-index

  2. 60

    h-index

  3. IEEE Access

    48 publications · 2.6K citations

    35

    h-index

  4. 33

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 38 publications · 3K citations

    30

    h-index

1–5