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Adversarial Text-to-Image Synthesis: A Review

arXiv 2101.09983 ↗First posted 25 Jan 2021cs.CVcited by 52

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Abstract (arXiv)

With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with significant progress in the last years regarding visual realism, diversity, and semantic alignment. However, the field still faces several challenges that require further research efforts such as enabling the generation of high-resolution images with multiple objects, and developing suitable and reliable evaluation metrics that correlate with human judgement. In this review, we contextualize the state of the art of adversarial text-to-image synthesis models, their development since their inception five years ago, and propose a taxonomy based on the level of supervision. We critically examine current strategies to evaluate text-to-image synthesis models, highlight shortcomings, and identify new areas of research, ranging from the development of better datasets and evaluation metrics to possible improvements in architectural design and model training. This review complements previous surveys on generative adversarial networks with a focus on text-to-image synthesis which we believe will help researchers to further advance the field.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). Builds on 134 of its 187 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

134 of 187 references.

References · 134

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Citing papers per year: 2021: 8; 2022: 10; 2023: 16; 2024: 12; 2025: 5; 2026: 1.

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  2. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 264
  3. 2022DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Modelscited by 230
  4. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 186
  5. 2023AGIQA-3K: An Open Database for AI-Generated Image Quality Assessmentcited by 142
  6. 2024Diffusion Models, Image Super-Resolution And Everything: A Surveycited by 63
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  9. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  10. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
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  5. 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 20
  6. 2023Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolutioncited by 19
  7. 2025Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategycited by 16
  8. 2021GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Networkcited by 15
  9. 2021ClimateGAN: Raising Climate Change Awareness by Generating Images of Floodscited by 14
  10. 2024CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIPcited by 14
  11. 2021TISE: Bag of Metrics for Text-to-Image Synthesis Evaluationcited by 13
  12. 2023Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generationcited by 13
  13. 2021AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and Stylecited by 11
  14. 2022Generalization in Neural Networks: A Broad Surveycited by 11
  15. 2024SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Timecited by 11
  16. 2023Dynamic Attention-Guided Diffusion for Image Super-Resolutioncited by 10
  17. 2024Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessmentcited by 10
  18. 2022Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generationcited by 9
  19. 2023Vision + Language Applications: A Surveycited by 8
  20. 2025PromptMap: An Alternative Interaction Style for AI-Based Image Generationcited by 8
  21. 2023About optimal loss function for training physics-informed neural networks under respecting causalitycited by 6
  22. 2021Combining Transformer Generators with Convolutional Discriminatorscited by 5
  23. 2024Separable Physics-Informed Neural Networks for the solution of elasticity problemscited by 5
  24. 2024From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identificationcited by 5
  25. 2022DT2I: Dense Text-to-Image Generation from Region Descriptionscited by 4
  26. 2023FIANCEE: Faster Inference of Adversarial Networks via Conditional Early Exitscited by 3
  27. 2024Generating Unseen Nonlinear Evolution in Sea Surface Temperature Using a Deep Learning-Based Latent Space Data Assimilation Frameworkcited by 3
  28. 2023Adversarial learning of neural user simulators for dialogue policy optimisationcited by 2
  29. 2022GR-GAN: Gradual Refinement Text-to-image Generationcited by 1
  30. 2024Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Modelscited by 1
  31. 2024Offline Evaluation of Set-Based Text-to-Image Generationcited by 1
  32. 2025TDRI: Two-Phase Dialogue Refinement and Co-Adaptation for Interactive Image Generationcited by 1
  33. 2025Towards Generalized and Training-Free Text-Guided Semantic Manipulationcited by 1
  34. 2021NFTGAN: Non-Fungible Token Art Generation Using Generative Adversarial Networkscited by 0
  35. 2022TeTIm-Eval: a novel curated evaluation data set for comparing text-to-image modelscited by 0
  36. 2023Artificial Intelligence Generated Coins for Size Comparisoncited by 0
  37. 2023A Simple and Effective Baseline for Attentional Generative Adversarial Networkscited by 0
  38. 2024Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue Systemcited by 0

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