这个编辑正确吗？面向推理感知图像编辑的多维度基准
Is This Edit Correct? A Multi-Dimensional Benchmark for Reasoning-Aware Image Editing
AI总结：
提出RE-Edit基准，从物理、环境、文化、因果和指代五个推理维度评估图像编辑系统，发现现有模型在隐式逻辑约束推理上存在不足，并引入轻量级推理引导后编辑基线。
摘要：
基于扩散的图像编辑在自然语言指令下实现了强大的视觉保真度，但大多数现有系统仍停留在表面指令遵循层面，没有推理真实用户请求中嵌入的隐式上下文约束。这常常导致视觉上合理但逻辑不一致的编辑。在这项工作中，我们引入了RE-Edit，一个面向推理感知图像编辑的基准，它从五个互补的推理维度评估图像编辑系统：物理、环境、文化、因果和指代。RE-Edit包含1000个精心策划的样本，每个样本的设计使得仅凭视觉合理性是不够的，正确的编辑需要满足隐式逻辑约束。为了支持细粒度分析，我们建立了维度对齐的评估标准，并对十个开源和两个商业图像编辑模型进行了全面研究。我们的结果表明，即使先进的系统也常常在隐式多维度推理上挣扎，尽管它们能产生高质量的视觉结果。我们进一步提出了一个轻量级的推理引导后编辑基线作为初步探索，说明了如何以模型无关的方式插入显式推理来帮助缓解此类失败。
英文摘要：
Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads to visually plausible but logically inconsistent edits. In this work, we introduce RE-Edit, a benchmark for REasoning-aware image Editing that evaluates image editing systems across five complementary reasoning dimensions: physical, environmental, cultural, causal, and referential. RE-Edit comprises 1,000 carefully curated samples, each designed such that visual plausibility alone is insufficient and correct editing requires satisfying implicit logical constraints. To support fine-grained analysis, we establish dimension-aligned evaluation criteria and conduct a comprehensive study of ten open-source and two commercial image editing models. Our results show that even advanced systems frequently struggle with implicit multi-dimensional reasoning despite producing high-quality visuals. We further present a lightweight reasoning-guided post-edit baseline as an initial exploration, illustrating how inserting explicit reasoning can help mitigate such failures in a model-agnostic manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
