ReMOVE：一种用于物体擦除的无参考度量
ReMOVE: A Reference-free Metric for Object Erasure
AI总结：
本文提出无参考度量ReMOVE，用于评估扩散模型图像编辑中的物体擦除效果，能区分移除与替换，并与人类感知一致。
摘要：
我们提出ReMOVE，一种新颖的无参考度量，用于评估基于扩散的图像编辑模型在生成后对物体擦除的效果。与LPIPS和CLIPScore等现有度量不同，ReMOVE解决了在实际场景中常见的、在没有参考图像的情况下评估图像修复的挑战。它能够有效区分物体移除与物体替换。由于图像生成的随机性，这是扩散模型中的一个关键问题。传统度量无法与图像修复的直观定义保持一致，该定义旨在（1）在掩码区域内无缝移除物体（2）同时保持背景的连续性。ReMOVE不仅与最先进的度量相关并与人类感知一致，而且还捕捉了图像修复过程中的细微方面，为生成输出提供了更细粒度的评估。
英文摘要：
We introduce $\texttt{ReMOVE}$, a novel reference-free metric for assessing object erasure efficacy in diffusion-based image editing models post-generation. Unlike existing measures such as LPIPS and CLIPScore, $\texttt{ReMOVE}$ addresses the challenge of evaluating inpainting without a reference image, common in practical scenarios. It effectively distinguishes between object removal and replacement. This is a key issue in diffusion models due to stochastic nature of image generation. Traditional metrics fail to align with the intuitive definition of inpainting, which aims for (1) seamless object removal within masked regions (2) while preserving the background continuity. $\texttt{ReMOVE}$ not only correlates with state-of-the-art metrics and aligns with human perception but also captures the nuanced aspects of the inpainting process, providing a finer-grained evaluation of the generated outputs.
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。
