mlsanityData to 7 Oct 2026

Generative AI in Vision: A Survey on Models, Metrics and Applications

arXiv 2402.16369 ↗First posted 26 Feb 2024cs.CV cs.AI cs.LGcited by 4

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Generative AI models have revolutionized various fields by enabling the creation of realistic and diverse data samples. Among these models, diffusion models have emerged as a powerful approach for generating high-quality images, text, and audio. This survey paper provides a comprehensive overview of generative AI diffusion and legacy models, focusing on their underlying techniques, applications across different domains, and their challenges. We delve into the theoretical foundations of diffusion models, including concepts such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling. Furthermore, we explore the diverse applications of these models in text-to-image, image inpainting, and image super-resolution, along with others, showcasing their potential in creative tasks and data augmentation. By synthesizing existing research and highlighting critical advancements in this field, this survey aims to provide researchers and practitioners with a comprehensive understanding of generative AI diffusion and legacy models and inspire future innovations in this exciting area of artificial intelligence.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 67 of its 90 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

67 of 90 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Text-to-image Diffusion Models in Generative AI: A Survey14 Mar
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
  3. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec

Other references · 64

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  5. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  6. 2014Going Deeper with Convolutionscited by 4,231
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  8. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
  9. 2015Variational Inference with Normalizing Flowscited by 2,161
  10. 2014Conditional Generative Adversarial Netscited by 2,152
  11. 2016Density estimation using Real NVPcited by 2,029
  12. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
38 more
  1. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  2. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  5. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  6. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,419
  7. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  8. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
  9. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  10. 2020Improved Techniques for Training Score-Based Generative Modelscited by 1,036
  11. 2022Diffusion Models in Vision: A Surveycited by 1,008
  12. 2019An Introduction to Variational Autoencoderscited by 997
  13. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  14. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translationcited by 897
  15. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  16. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
  17. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  18. 2021Vector-quantized Image Modeling with Improved VQGANcited by 656
  19. 2017Generative Adversarial Networks: An Overviewcited by 588
  20. 2018Generative Image Inpainting with Contextual Attentioncited by 578
  21. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  22. 2013Generating Sequences With Recurrent Neural Networkscited by 549
  23. 2016f-GAN: Training Generative Neural Samplers using Variational Divergence Minimizationcited by 504
  24. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  25. 2017Towards Principled Methods for Training Generative Adversarial Networkscited by 460
  26. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  27. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  28. 2021SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Modelscited by 415
  29. 2016Tutorial on Variational Autoencoderscited by 404
  30. 2018Assessing Generative Models via Precision and Recallcited by 401
  31. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 381
  32. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 320
  33. 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
  34. 2021How to Train Your Energy-Based Modelscited by 271
  35. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  36. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  37. 2021FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flowscited by 225
  38. 2021Large Scale Image Completion via Co-Modulated Generative Adversarial Networkscited by 219

50 most cited of 64 All 64 (JSON)

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2024Rapid Review of Generative AI in Smart Medical Applicationscited by 2
  2. 2025GenAI Confessions: Black-box Membership Inference for Generative Image Modelscited by 2
  3. 2025GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learningcited by 2
  4. 2025SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detectioncited by 1

Similar, not cited

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  1. 2023State of the Art on Diffusion Models for Visual Computing
  2. 2025Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
  3. 2024Diffusion Models in Low-Level Vision: A Survey
  4. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications
  5. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  6. 2024Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions
  7. 2022Implementing and Experimenting with Diffusion Models for Text-to-Image Generation
  8. 2024An overview of diffusion models for generative artificial intelligence