让图像编辑像人一样理解局部与整体关系，提升修改一致性
Region in Context: Text-condition Image editing with Human-like semantic reasoning
- 通过双层语义对齐，让每个区域理解全局上下文
- 生成场景级描述指导整体结构，区域级描述引导细节修改
- 适合需要精准且自然图像修改的视觉任务
近期研究在基于文本定位并编辑图像区域方面取得进展，但多数方法将区域孤立处理，仅依赖局部线索，忽视各部分对整体视觉与语义构图的贡献。这常导致编辑不一致、过渡不自然或图像失真。本文提出「Region in Context」框架，模拟人类对场景的整体推理能力，实现视觉与语言的多层次语义对齐。该方法通过双重引导机制：区域在全图上下文中表示，并与区域级描述对齐；同时整个图像与由大视觉语言模型生成的场景级描述匹配。这些描述作为显式内容参考，指导局部修改与全局结构保持一致。实验表明，该方法能生成更连贯、指令对齐的结果。代码已开源：https://github.com/thuyvuphuong/Region-in-Context.git
原文摘要 · Abstract (English)
Recent research has made significant progress in localizing and editing image regions based on text. However, most approaches treat these regions in isolation, relying solely on local cues without accounting for how each part contributes to the overall visual and semantic composition. This often results in inconsistent edits, unnatural transitions, or loss of coherence across the image. In this work, we propose Region in Context, a novel framework for text-conditioned image editing that performs multilevel semantic alignment between vision and language, inspired by the human ability to reason about edits in relation to the whole scene. Our method encourages each region to understand its role within the global image context, enabling precise and harmonized changes. At its core, the framework introduces a dual-level guidance mechanism: regions are represented with full-image context and aligned with detailed region-level descriptions, while the entire image is simultaneously matched to a comprehensive scene-level description generated by a large vision-language model. These descriptions serve as explicit verbal references of the intended content, guiding both local modifications and global structure. Experiments show that it produces more coherent and instruction-aligned results. Code is available at: https://github.com/thuyvuphuong/Region-in-Context.git
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