首个专为文本编辑图像设计的感知评估基准，提升真实感判断准确性。
IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment
- 构建包含3010条主观评分的图文编辑评测数据集
- 提出IE-QA模型，显著优于传统指标与人类感知一致性
- 适合图像生成、评价算法研究者参考使用
近年来，文本驱动图像编辑技术进展迅速，但其评估仍面临挑战。与仅依赖文本生成图像不同，该任务需同时依据文本和源图像进行编辑，且结果与原图存在动态语义关联。现有方法多聚焦于图文对齐，未能贴合人类感知。本文提出文本驱动图像编辑基准（IE-Bench），包含多样源图、多种编辑提示及对应结果，共收集25名受试者提供的3,010条平均意见分数（MOS）。同时引入多模态、源感知的质量评估方法IE-QA。据我们所知，IE-Bench是首个针对文本驱动图像编辑的主观质量评估数据集与模型。大量实验表明，IE-QA在主观一致性上显著优于已有指标。相关数据与代码将公开共享。
原文摘要 · Abstract (English)
Recent advances in text-driven image editing have been significant, yet the task of accurately evaluating these edited images continues to pose a considerable challenge. Different from the assessment of text-driven image generation, text-driven image editing is characterized by simultaneously conditioning on both text and a source image. The edited images often retain an intrinsic connection to the original image, which dynamically change with the semantics of the text. However, previous methods tend to solely focus on text-image alignment or have not aligned with human perception. In this work, we introduce the Text-driven Image Editing Benchmark suite (IE-Bench) to enhance the assessment of text-driven edited images. IE-Bench includes a database contains diverse source images, various editing prompts and the corresponding results different editing methods, and total 3,010 Mean Opinion Scores (MOS) provided by 25 human subjects. Furthermore, we introduce IE-QA, a multi-modality source-aware quality assessment method for text-driven image editing. To the best of our knowledge, IE-Bench offers the first IQA dataset and model tailored for text-driven image editing. Extensive experiments demonstrate IE-QA's superior subjective-alignments on the text-driven image editing task compared with previous metrics. We will make all related data and code available to the public.
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