Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding
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AI media generation models have been trained on datasets that reflect historical erasure and bias.
“AI media generation, specifically image generation models that translate free-form text prompts into visuals [87, 94, 95], have been trained on datasets that reflect this historical erasure and bias.”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [95]
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De novo design of protein structure and function with RFdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al. (2023) · Nature · doi:10.1038/s41586-023-06415-8 · Cited by 2,325 · see citing works
Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101896 · Cited by 800 · see citing works
The Cognitive Architecture of Digital Externalization
Skulmowski, A. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09818-1 · Cited by 76 · see citing works
The Impact of Generative AI Images on Consumer Attitudes in Advertising
Zhang, L., & Hur, C. (2025) · Administrative Sciences · doi:10.3390/admsci15100395 · Cited by 28 · see citing works
Qadri, R., Mirowski, P., & Denton, R. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714049 · Cited by 12 · see citing works
Cosmos World Foundation Model Platform for Physical AI
NVIDIA, :, Agarwal, N., Ali, A., Bala, M., Balaji, Y., Barker, E., Cai, T., Chattopadhyay, P., Chen, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2501.03575 · Cited by 13 · see citing works
Qadri, R., Mirowski, P., Gabriellan, A., Mehr, F., Gupta, H., Karimi, P., & Denton, R. (2024) · arXiv · doi:10.48550/arxiv.2412.14077 · Cited by 5 · see citing works
CiteME: Can Language Models Accurately Cite Scientific Claims?
Press, O., Hochlehnert, A., Prabhu, A., Udandarao, V., Press, O., & Bethge, M. (2024) · arXiv · doi:10.48550/arxiv.2407.12861 · Cited by 4
Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation
Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works
SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix
Dai, P., Tan, F., Xu, Q., Futschik, D., Du, R., Fanello, S., Qi, X., & Zhang, Y. (2024) · arXiv · doi:10.48550/arxiv.2407.00367
Akdemir, K., & Yanardag, P. (2024) · arXiv · doi:10.48550/arxiv.2406.02820
- Authors
- Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, K., Lopes, R. G., Ayan, B. K., Salimans, T., Ho, J., Fleet, D., & Norouzi, M.
- Venue
- Advances in Neural Information Processing Systems 35
- Published
- 2022
- Pages
- 36479-36494
- ISSN
- 1049-5258 (ISSN-L)
- OpenAlex
- W7133185348
- Cited by
- 559
- References
- 0
- Citations by year
- 2026: 452 · 2025: 86 · 2024: 8 · 2023: 10 · 2022: 3
- FWCI
- 9.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- neural information processing systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- text-to-image generation, diffusion models, realistic image synthesis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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