mlsanityData to 14 Oct 2026

Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation

arXiv 2209.14046 ↗First posted 28 Sep 2022cs.CVcited by 6

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Abstract (arXiv)

As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well. However, directly utilizing the limited information in one sentence misses some key attribute descriptions, which are the crucial factors to describe an image accurately. To alleviate the above problem, we propose an effective text representation method with the complements of attribute information. Firstly, we construct an attribute memory to jointly control the text-to-image generation with sentence input. Secondly, we explore two update mechanisms, sample-aware and sample-joint mechanisms, to dynamically optimize a generalized attribute memory. Furthermore, we design an attribute-sentence-joint conditional generator learning scheme to align the feature embeddings among multiple representations, which promotes the cross-modal network training. Experimental results illustrate that the proposed method obtains substantial performance improvements on both the CUB (FID from 14.81 to 8.57) and COCO (FID from 21.42 to 12.39) datasets.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 46 of its 65 references that are in the map. First posted on arXiv 28 Sep 2022.

Builds on

46 of 65 references.

References · 46

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  7. 2016Improved Techniques for Training GANscited by 2,970
  8. 2015VQA: Visual Question Answeringcited by 2,506
  9. 2014Conditional Generative Adversarial Netscited by 2,162
  10. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
  11. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  12. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
34 more
  1. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 519
  2. 2021GAN Inversion: A Surveycited by 367
  3. 2019Multi-Label Image Recognition with Graph Convolutional Networkscited by 322
  4. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  5. 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
  6. 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
  7. 2017Geometric GANcited by 233
  8. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  9. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  10. 2020General Multi-label Image Classification with Transformerscited by 127
  11. 2019Object-driven Text-to-Image Synthesis via Adversarial Trainingcited by 117
  12. 2018Inferring Semantic Layout for Hierarchical Text-to-Image Synthesiscited by 101
  13. 2019Semantic Object Accuracy for Generative Text-to-Image Synthesiscited by 89
  14. 2021Boundary Proposal Network for Two-Stage Natural Language Video Localizationcited by 88
  15. 2021Deconfounded Video Moment Retrieval with Causal Interventioncited by 84
  16. 2018Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Networkcited by 64
  17. 2021Improving Text-to-Image Synthesis Using Contrastive Learningcited by 62
  18. 2019Semantics Disentangling for Text-to-Image Generationcited by 61
  19. 2020Memory Efficient Class-Incremental Learning for Image Classificationcited by 50
  20. 2021DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesiscited by 45
  21. 2021Cycle-Consistent Inverse GAN for Text-to-Image Synthesiscited by 27
  22. 2017Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captionercited by 21
  23. 2021Conditional GANs with Auxiliary Discriminative Classifiercited by 20
  24. 2020TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generatorcited by 19
  25. 2018ChatPainter: Improving Text to Image Generation using Dialoguecited by 18
  26. 2020VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Taskscited by 12
  27. 2022Memory-Driven Text-to-Image Generationcited by 10
  28. 2021MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identificationcited by 9
  29. 2020SegAttnGAN: Text to Image Generation with Segmentation Attentioncited by 6
  30. 2020Leveraging Visual Question Answering to Improve Text-to-Image Synthesiscited by 3
  31. 2019GILT: Generating Images from Long Textcited by 2
  32. 2019Collapse Resistant Deep Convolutional GAN for Multi-Object Image Generationcited by 2
  33. 2021Multi-Class Multi-Instance Count Conditioned Adversarial Image Generationcited by 1
  34. 2022F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networkscited by 1

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Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 1.

6 papers.

  1. 2023Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Modelscited by 117
  2. 2023Attribute-Centric Compositional Text-to-Image Generationcited by 19
  3. 2024DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Drivingcited by 11
  4. 2023VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Datacited by 7
  5. 2023Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generationcited by 0
  6. 2025Food Image Generation on Multi-Noun Categoriescited by 0