Abstract
Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. These problems remain difficult, especially when it is important to preserve semantic structures. Traditional image-level similarity metrics are of limited use, since the semantics of an image are high-level, and not strongly governed by pixel-wise faithfulness to an original image. Towards filling this gap, we introduce SAMScore, a generic semantic structural similarity metric for evaluating the faithfulness of image translation models. SAMScore is based on the recent high-performance Segment Anything Model (SAM), which can perform semantic similarity comparisons with standout accuracy. We applied SAMScore on 19 image translation tasks, and found that it is able to outperform all other competitive metrics on all of the tasks. We envision that SAMScore will prove to be a valuable tool that will help to drive the vibrant field of image translation, by allowing for more precise evaluations of new and evolving translation models. The code is available at this https URL.
Abstract (translated)
图像翻译具有广泛的应用，例如风格转移和模式转换，通常旨在生成具有高度真实感和准确性的图像。这些问题仍然非常困难，特别是在保护语义结构非常重要的情况下。传统的图像级相似度 metrics 有限使用，因为图像的语义含义是高层次的，并且不是像素级忠实于原始图像的程度。为了填补这个差距，我们介绍了SamadScore，一个通用的语义结构相似度 metrics，用于评估图像翻译模型的准确性。SamadScore 基于最近的高性能分块模型(Sam)，可以进行语义相似度比较，并有出色的精度表现。我们使用了SamadScore 对 19 个图像翻译任务进行了应用，发现它可以在所有任务中比所有其他竞争指标表现更好。我们展望着SamadScore 将成为一个重要的工具，可以帮助推动图像翻译领域的发展，允许更精确的评估新的和进化的翻译模型。代码在此httpsURL上可用。
URL
https://arxiv.org/abs/2305.15367
