Text-to-image Generation

Text-To-Image Generation is a computational framework within artificial intelligence that synthesizes visual content from textual descriptions through deep learning models, distinguishing itself by directly mapping semantic language structures to corresponding image representations.

990

Publications

79.1K

Citations

3.1K

Authors

590

Institutions

Publications per year

2017–2026

961

Authors

3.1K

  1. DC
    Daniel Cohen‐Or

    Tel Aviv University, Israel

    17 publications · 3K citations

    16

    h-index

  2. HZ
    Han Zhang

    Google (United States), United States

    15 publications · 4K citations

    14

    h-index

  3. TD
    Tali Dekel

    Google (United States), United States

    10 publications · 1.6K citations

    11

    h-index

  4. KA
    Kfir Aberman

    Google (United States), United States

    10 publications · 3.5K citations

    11

    h-index

  5. JZ
    Jun-Yan Zhu

    Carnegie Mellon University, United States

    9 publications · 1.6K citations

    11

    h-index

1–5 of 3.1K

Venues

  1. arXiv (Cornell University)

    Repository · 261 publications · 22K citations

    58

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 22 publications · 1.5K citations

    18

    h-index

  3. 15

    h-index

  4. ACM Transactions on Graphics

    TOG · 15 publications · 1.5K citations

    14

    h-index

  5. IEEE Access

    14 publications · 603 citations

    13

    h-index

1–5