mlsanityData to 14 Oct 2026

DE-Net: Dynamic Text-guided Image Editing Adversarial Networks

arXiv 2206.01160 ↗First posted 2 Jun 2022cs.CV cs.MMcited by 12

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Abstract (arXiv)

Text-guided image editing models have shown remarkable results. However, there remain two problems. First, they employ fixed manipulation modules for various editing requirements (e.g., color changing, texture changing, content adding and removing), which results in over-editing or insufficient editing. Second, they do not clearly distinguish between text-required and text-irrelevant parts, which leads to inaccurate editing. To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements. (ii) a Composition Predictor (Comp-Pred) which predicts the composition weights for DEBlock according to the inference on target texts and source images. (iii) a Dynamic text-adaptive Convolution Block (DCBlock) which queries source image features to distinguish text-required parts and text-irrelevant parts. Extensive experiments demonstrate that our DE-Net achieves excellent performance and manipulates source images more correctly and accurately. Code is available at urlhttps://github.com/tobran/DE-Net.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 30 of its 38 references that are in the map. First posted on arXiv 2 Jun 2022.

Builds on

30 of 38 references.

References · 30

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18 more
  1. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
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  7. 2019ManiGAN: Text-Guided Image Manipulationcited by 156
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  12. 2019Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generationcited by 75
  13. 2021Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Modelcited by 34
  14. 2020Lightweight Generative Adversarial Networks for Text-Guided Image Manipulationcited by 33
  15. 2020Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approachcited by 29
  16. 2020Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructionscited by 25
  17. 2021Towards Open-World Text-Guided Face Image Generation and Manipulationcited by 25
  18. 2022ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generationcited by 13

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 6; 2024: 3; 2025: 2.

12 papers.

  1. 2022Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpaintingcited by 233
  2. 2023GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesiscited by 85
  3. 2023Alchemist: Parametric Control of Material Properties with Diffusion Modelscited by 38
  4. 2024FreeCompose: Generic Zero-Shot Image Composition with Diffusion Priorcited by 16
  5. 2023Text-to-image Editing by Image Information Removalcited by 12
  6. 2023DialogPaint: A Dialog-based Image Editing Modelcited by 8
  7. 2023Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesiscited by 8
  8. 2024A Survey of Multimodal Composite Editing and Retrievalcited by 5
  9. 2023Robust Text-driven Image Editing Method that Adaptively Explores Directions in Latent Spaces of StyleGAN and CLIPcited by 1
  10. 2024AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusioncited by 1
  11. 2025Towards Generalized and Training-Free Text-Guided Semantic Manipulationcited by 1
  12. 2025An Evaluation of Hybrid Annotation Workflows on High-Ambiguity Spatiotemporal Video Footagecited by 1