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June 20, 2026Artificial Intelligence ReviewOpen Access

Beyond image generation: visual data analysis with diffusion models—a comprehensive survey

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Authors

BOBartłomiej OlechnoWarsaw University of TechnologyJMJacek MańdziukWarsaw University of Technology

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Overview

Survey reviews non-generative applications of diffusion models in visual data, indicating key insights and challenges.

Key Points

  • The aim is to examine non-generative applications of diffusion models in visual data analysis and provide an organized taxonomy of their uses.
  • Systematic review of research papers from top AI/ML conferences, journals, and arXiv.
  • Analysis divides applications into four categories: content detection, action understanding, spatiotemporal view estimation, and representation learning.
  • Highlighting advantages and challenges of diffusion models in several visual analysis tasks.
  • Diffusion models perform better in tasks like pose estimation and anomaly detection but are 10-100 times slower than alternatives.
  • The models handle ambiguous ground truth better and quantify uncertainty more effectively.
  • Hybrid approaches combining diffusion and discriminative methods are promising for improving efficiency.

Cite This Study

Olechno et al. (2026) studied this question.

synapsesocial.com/papers/6a3631fbdb0793dc1a538b8bhttps://doi.org/10.1007/s10462-026-11615-5
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