SPAN:学习场景图和图像与变压器之间的相似性
IEEE transactions on pattern analysis and machine intelligence
|November 7, 2025
概括
我们介绍了SPAN,这是一个用于测量场景图形和图像相似性的新框架. 这种方法改善了场景图生成评估,并使下游应用程序更好.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 场景图表生成对人工智能应用至关重要,但缺乏有效的评估指标.
- 像Recall@K这样的当前指标对噪音敏感,不能捕捉整体语义差异.
- 这限制了下游任务中生成场景图的实际使用.
研究的目的:
- 提出第一个框架,SPAN (Scene graPh-imAge coNtrastive学习),用于学习场景图和图像之间的相似性.
- 引入一个新的评估指标,R-Precision,用于场景图表生成.
- 建立新的基准来评估场景图形图像相似性.
主要方法:
- 开发了一个图形转换器和一个图像转换器,以在共享的潜空间中对齐场景图形和图像.
- 引入了一种新的图形序列化技术,将场景图形转换为具有结构编码的序列.
- 建议R-Precision,一个图像检索准确度指标,用于场景图表生成评估.
主要成果:
- 该SPAN框架有效地测量场景图和图像相似性.
- 作为场景图表生成的评估指标,R-Precision展示了卓越的性能.
- 在视觉基因组和开放图像数据集上建立了新的基准,验证了SPAN的有效性.
结论:
- SPAN提供了一种强大的方法来学习场景图像相似性,解决现有指标的局限性.
- 拟议的R-Precision指标为场景图表生成提供了更可靠的评估.
- SPAN显示出作为各种AI应用程序的场景图形编码器的巨大潜力.
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