The development of vision-language and generative models has significantly advanced text-guided image editing, which seeks \textit{preservation} of core elements in the source image while implementing \textit{modifications} based on the target text. However, in the absence of evaluation metrics specifically tailored for text-guided image editing, existing metrics are limited in balancing the consideration of preservation and modification. Especially, our analysis reveals that CLIPScore, the most commonly used metric, tends to favor modification and ignore core attributes to be preserved, resulting in inaccurate evaluations. To address this problem, we propose \texttt{AugCLIP}, \black{which balances preservation and modification by estimating the representation of an ideal edited image that aligns with the target text with minimum alteration on the source image. We augment detailed textual descriptions on the source image and the target text using a multi-modal large language model, to model a hyperplane that separates CLIP space into source or target. The representation of the ideal edited image is an orthogonal projection of the source image into the hyperplane, which encapsulates the relative importance of each attribute considering the interdependent relationships.} Our extensive experiments on five benchmark datasets, encompassing a diverse range of editing scenarios, demonstrate that \texttt{AugCLIP} aligns remarkably well with human evaluation standards compared to existing metrics. The code for evaluation will be open-sourced to contribute to the community.

本研究针对文本导向图像编辑中缺乏专门评估指标的问题，提出了一种新颖的评估方法AugCLIP，能够平衡图像的核心元素保留与目标文本要求的修改。通过引入多模态的大型语言模型，AugCLIP有效提高了对理想编辑图像表现的评估，与现有度量相比，其结果更贴近人类评估标准，有望推动文本导向图像编辑领域的进一步发展。

基于增强驱动的度量方法：平衡文本导向图像编辑中的保留与修改