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SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation.

Yunxiang Li1, Meixu Chen1, Kai Wang1

  • 1Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

IEEE Transactions on Artificial Intelligence
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

We developed SAMScore, a new metric for evaluating image translation models. SAMScore accurately measures content structure similarity, outperforming existing methods in diverse image translation tasks.

Keywords:
Image translationevaluation metricgenerative artificial intelligence

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Image translation, including style transfer and modality conversion, aims for realistic and faithful image generation.
  • Preserving content structure in image translation remains a significant challenge.
  • Existing image-level similarity metrics often fail to capture high-level structural content accurately.

Purpose of the Study:

  • Introduce SAMScore, a novel content structural similarity metric for evaluating image translation models.
  • Address the limitations of traditional metrics in assessing structural faithfulness.
  • Provide a more accurate evaluation tool for the field of image translation.

Main Methods:

  • Leveraged the high-performance Segment Anything Model (SAM) to develop SAMScore.
  • Designed SAMScore as a generic metric applicable across various image translation tasks.
  • Compared SAMScore against existing metrics on 19 diverse image translation tasks.

Main Results:

  • SAMScore demonstrated superior performance in evaluating faithfulness across all tested image translation tasks.
  • The metric achieved standout accuracy in content similarity comparisons.
  • SAMScore outperformed all competitive metrics in the evaluation.

Conclusions:

  • SAMScore is a valuable new tool for the precise evaluation of image translation models.
  • The metric facilitates more accurate assessments of realism and structural faithfulness.
  • SAMScore is expected to drive advancements in the field of image translation.