mlsanityData to 14 Oct 2026

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

arXiv 1711.10485 ↗First posted 28 Nov 2017cs.CVcited by 664

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Cited by 182 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different subregions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.

Key facts. Cited by 664 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2017.

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Citing papers per year: 2021: 106; 2022: 122; 2023: 222; 2024: 131; 2025: 61; 2026: 22.

664 papers.

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  1. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
  3. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  4. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,614
  5. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
  6. 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,742
  7. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
  8. 2022Multi-Concept Customization of Text-to-Image Diffusioncited by 1,106
  9. 2021Transformers in Vision: A Surveycited by 1,013
  10. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 991
  11. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 972
  12. 2021Blended Diffusion for Text-driven Editing of Natural Imagescited by 927
38 more
  1. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 781
  2. 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 774
  3. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
  4. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 703
  5. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 702
  6. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
  7. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 670
  8. 2023Muse: Text-To-Image Generation via Masked Generative Transformerscited by 648
  9. 2022Compositional Visual Generation with Composable Diffusion Modelscited by 624
  10. 2023MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editingcited by 624
  11. 2023NExT-GPT: Any-to-Any Multimodal LLMcited by 615
  12. 2022All are Worth Words: A ViT Backbone for Diffusion Modelscited by 502
  13. 2023Scaling up GANs for Text-to-Image Synthesiscited by 492
  14. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  15. 2022Blended Latent Diffusioncited by 432
  16. 2023TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answeringcited by 428
  17. 2021GAN Inversion: A Surveycited by 367
  18. 2022TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Textscited by 359
  19. 2023Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Modelscited by 359
  20. 2023InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuningcited by 345
  21. 2023Ablating Concepts in Text-to-Image Diffusion Modelscited by 317
  22. 2023Training-Free Layout Control with Cross-Attention Guidancecited by 313
  23. 2022Text2LIVE: Text-Driven Layered Image and Video Editingcited by 312
  24. 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 307
  25. 2023Composer: Creative and Controllable Image Synthesis with Composable Conditionscited by 304
  26. 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 302
  27. 2023DreamLLM: Synergistic Multimodal Comprehension and Creationcited by 297
  28. 2021VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalizationcited by 287
  29. 2023Text-to-image Diffusion Models in Generative AI: A Surveycited by 265
  30. 2023Generating Images with Multimodal Language Modelscited by 260
  31. 2023T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-image Generationcited by 260
  32. 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
  33. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  34. 2023Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translationcited by 253
  35. 2021Attention Mechanisms in Computer Vision: A Surveycited by 232
  36. 2023Break-A-Scene: Extracting Multiple Concepts from a Single Imagecited by 231
  37. 2022DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Modelscited by 230
  38. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224

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