面向可扩展的文本引导图像编辑人类对齐基准
Towards Scalable Human-aligned Benchmark for Text-guided Image Editing
AI总结：
针对文本引导图像编辑缺乏标准评估方法的问题，提出了大规模、全自动的人类对齐基准HATIE，通过结合多维度评分对齐人类感知，实现对多种最先进模型可靠且全方位的评估。
摘要：
近期已有多种文本引导的图像编辑模型被提出。然而，由于该任务的主观性，目前缺乏广泛接受的标准评估方法，使得研究人员依赖人工用户研究。为解决此问题，我们提出了一种新颖的文本引导图像编辑人类对齐基准（HATIE）。它提供了一个涵盖广泛编辑任务的大规模基准集，允许进行可靠的评估，而不局限于特定的易于评估的案例。同时，HATIE提供了一个全自动且全方位的评估流程。具体而言，我们结合了衡量编辑各个方面的多个分数，以与人类感知对齐。我们实证验证了HATIE的评估在各个方面确实与人类对齐，并提供了几个最先进模型的基准结果，以对其性能提供更深入的见解。
英文摘要：
A variety of text-guided image editing models have been proposed recently. However, there is no widely-accepted standard evaluation method mainly due to the subjective nature of the task, letting researchers rely on manual user study. To address this, we introduce a novel Human-Aligned benchmark for Text-guided Image Editing (HATIE). Providing a large-scale benchmark set covering a wide range of editing tasks, it allows reliable evaluation, not limited to specific easy-to-evaluate cases. Also, HATIE provides a fully-automated and omnidirectional evaluation pipeline. Particularly, we combine multiple scores measuring various aspects of editing so as to align with human perception. We empirically verify that the evaluation of HATIE is indeed human-aligned in various aspects, and provide benchmark results on several state-of-the-art models to provide deeper insights on their performance.
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。
