Summary of Generative Ai in Vision: a Survey on Models, Metrics and Applications, by Gaurav Raut and Apoorv Singh
Generative AI in Vision: A Survey on Models, Metrics and Applications
by Gaurav Raut, Apoorv Singh
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper provides a comprehensive overview of generative AI diffusion and legacy models, exploring their underlying techniques, applications across different domains, and challenges. Specifically, it delves into the theoretical foundations of diffusion models, including denoising diffusion probabilistic models (DDPM) and score-based generative modeling. The survey also showcases diverse applications of these models in text-to-image synthesis, image inpainting, image super-resolution, and other creative tasks. By synthesizing existing research and highlighting critical advancements, this survey aims to provide researchers and practitioners with a comprehensive understanding of generative AI diffusion and legacy models, inspiring future innovations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence can create new data that is realistic and diverse. It focuses on a type of AI called “diffusion models” that are good at making images, text, and sounds. The authors explain the basics of these models, like denoising diffusion probabilistic models (DDPM) and score-based generative modeling. They also show how these models can be used for things like creating new images from texts, filling in gaps in old pictures, and making high-resolution copies of low-quality photos. |
Keywords
* Artificial intelligence * Diffusion * Image inpainting * Image synthesis * Super resolution
