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Irrelevant region preserving for counterfactual image manipulation
Yinuo Peng1, Yuxuan Wang1, Chenyue Wang1
1Chongqing Normal University, National Center of Applied Mathematics, Chongqing, 401331, China.
Scientific Reports
|November 29, 2025
Summary
This study introduces a novel image editing method for multimodal learning, enhancing complex edits while preserving unrelated image attributes. The approach improves semantic representation and editing accuracy for better image quality.
Area of Science:
- Computer Vision
- Multimodal Learning
- Artificial Intelligence
Background:
- Image editing is a key research area in multimodal learning.
- Existing methods using Contrastive-Language-Image-Pretraining (CLIP) struggle with complex edits and attribute disentanglement.
Purpose of the Study:
- To propose an image editing method that handles complex edits and protects irrelevant attributes.
- To improve semantic representation and editing accuracy in image manipulation.
Main Methods:
- A novel structure with a cross-attention mechanism for enhanced text-image feature fusion.
- A mask-controlled approach to maintain the semantics of unchanged image regions.
Main Results:
- The proposed method achieves comprehensive semantic representation and accurate editing.
- Experimental results demonstrate superior performance in image quality compared to existing methods.
Conclusions:
- The developed method effectively addresses complex image editing challenges.
- It successfully balances semantic editing with the preservation of irrelevant image attributes.
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