mlsanityData to 14 Oct 2026

Text-Guided Mask-free Local Image Retouching

arXiv 2212.07603 ↗First posted 15 Dec 2022cs.CVcited by 6

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Abstract (arXiv)

In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning. Most currently available text-guided methods, however, rely on object-level supervision to constrain the region that may be modified. This not only makes it more challenging to develop these algorithms, but it also limits how widely deep learning can be used for image retouching. In this paper, we offer a text-guided mask-free image retouching approach that yields consistent results to address this concern. In order to perform image retouching without mask supervision, our technique can construct plausible and edge-sharp masks based on the text for each object in the image. Extensive experiments have shown that our method can produce high-quality, accurate images based on spoken language. The source code will be released soon.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 15 Dec 2022.

Builds on

22 of 25 references.

References · 22

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10 more
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Cited by year

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

6 papers.

  1. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  2. 2024MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Modelscited by 21
  3. 2025Multi-turn Consistent Image Editingcited by 18
  4. 2024High-Quality Entity Segmentation and Groundingcited by 13
  5. 2025MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillationcited by 12
  6. 2025MaSS13K: A Matting-level Semantic Segmentation Benchmarkcited by 2