SAMScore: A Semantic Structural Similarity Metric for Image Translation
  Evaluation
Yunxiang Li Meixu Chen Wenxuan Yang Kai Wang Jun Ma Alan C. Bovik You Zhang
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 https://github.com/Kent0n-Li/SAMScore.