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
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3.1K
Authors
590
Institutions
Publications per year
2017–2026
961
Parents
990
arXiv (Cornell University) · 2023 · 2.4K citations · Full text
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani et al. · 2023 · 1.9K citations
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
Tao Xu, Pengchuan Zhang, Qiuyuan Huang et al. · 2018 · 1.9K citations
Synthetic Data for Text Localisation in Natural Images
Ankush Gupta, Andrea Vedaldi, Andrew Zisserman · 2016 · 1.5K citations
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Google (United States)
Mountain View, United States
30 h-index
172 publications
33.5K citations

Tel Aviv University
Tel Aviv, Israel
18 h-index
60 publications
8.7K citations

Peking University
Beijing, China
18 h-index
54 publications
3.5K citations
Adobe Systems (United States)
San Jose, United States
18 h-index
64 publications
8.2K citations
Tsinghua University
Beijing, China
17 h-index
37 publications
4K citations
Carnegie Mellon University
Pittsburgh, United States
17 h-index
40 publications
5.4K citations

Zhejiang University
Hangzhou, China
17 h-index
50 publications
4.9K citations
Tencent (China)
Shenzhen, China
15 h-index
40 publications
9K citations
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Repository · 261 publications · 22K citations
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Conference · 22 publications · 1.5K citations
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Conference · 16 publications · 3.4K citations
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TOG · 15 publications · 1.5K citations
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14 publications · 603 citations
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