Summary of Sequential Posterior Sampling with Diffusion Models, by Tristan S.w. Stevens et al.
Sequential Posterior Sampling with Diffusion Models
by Tristan S.W. Stevens, Oisín Nolan, Jean-Luc Robert, Ruud J.G. van Sloun
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes an innovative approach to improve the efficiency of sequential diffusion posterior sampling in conditional image synthesis, specifically for ultrasound imaging. The authors introduce a novel video vision transformer (ViViT) transition model that leverages previous diffusion outputs to initialize the reverse diffusion trajectory at a lower noise scale, significantly reducing the number of iterations required for convergence. This method accelerates inference 25 times while maintaining performance comparable to a full diffusion trajectory. Moreover, the addition of a transition model improves PSNR up to 8% in cases with severe motion. The proposed approach enables real-time posterior sampling and opens up new possibilities for diffusion models in imaging and other domains requiring real-time inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible to use computer algorithms called “diffusion models” for real-time applications like medical imaging. These algorithms are usually very slow because they need to make many calculations, but the authors found a way to speed them up by using information from previous images. They tested their approach on ultrasound images of the heart and showed that it works much faster than before while still producing high-quality results. This could be useful for medical imaging and other areas where quick decisions are needed. |
Keywords
» Artificial intelligence » Diffusion » Image synthesis » Inference » Vision transformer
