SAMScore:用于图像翻译评估的内容结构相似度指标
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
概括
我们开发了SAMScore,这是一个用于评估图像翻译模型的新指标. SAMScore准确地测量了内容结构相似性,在各种图像翻译任务中表现优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 图像翻译,包括风格转移和模式转换,旨在实现现实的和忠实的图像生成.
- 在图像翻译中保留内容结构仍然是一个重大挑战.
- 现有的图像级相似度指标往往无法准确地捕捉高层结构内容.
研究的目的:
- 介绍SAMScore,这是一个新的内容结构相似度指标,用于评估图像翻译模型.
- 解决评估结构忠实性的传统指标的局限性.
- 为图像翻译领域提供更准确的评估工具.
主要方法:
- 利用高性能的任何细分模型 (SAM) 来开发SAMScore.
- 设计SAMScore作为一种通用的指标,适用于各种图像翻译任务.
- 将SAMScore与19个不同的图像翻译任务的现有指标进行了比较.
主要成果:
- 在所有经过测试的图像翻译任务中,SAMScore在评估可信度方面表现出卓越的表现.
- 该指标在内容相似性比较中取得了突出的准确性.
- 在评估中,SAMScore的表现超过了所有竞争指标.
结论:
- SAMScore是一个有价值的新工具,用于精确评估图像翻译模型.
- 该指标有助于更准确地评估现实主义和结构忠诚度.
- 预计SAMScore将推动图像翻译领域的进步.
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