Tutorial on Diffusion Models for Imaging and Vision

The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some longstanding shortcomings in previous approaches. The goal of this tutorial is to discuss the essential ideas underlying these diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these tools to solve other problems.

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Publication Details

Journal
Foundations and Trends® in Computer Graphics and Vision
Published
2024-12-18
DOI
https://doi.org/10.1561/0600000112
Citations
20
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
7.90
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article

Tutorial on Diffusion Models for Imaging and Vision

Stanley M. H. Chan
20 citations
Foundations and Trends® in Computer Graphics and Vision
Mathematical Biology Tumor Growth
7.90
article

Tutorial on Diffusion Models for Imaging and Vision

Stanley M. H. Chan
article en
20 citations

Abstract

The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some longstanding shortcomings in previous approaches. The goal of this tutorial is to discuss the essential ideas underlying these diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these tools to solve other problems.

Foundations and Trends® in Computer Graphics and VisionVol. 16(4)
Purdue University West Lafayette (US)
Openalex Percentile: Top 2%
Mathematical Biology Tumor Growth
7.90
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