Scene graph generation has emerged as a prominent research field in computer
vision, witnessing significant advancements in the recent years. However,
despite these strides, precise and thorough definitions for the metrics used to
evaluate scene graph generation models are lacking. In this paper, we address
this gap in the literature by providing a review and precise definition of
commonly used metrics in scene graph generation. Our comprehensive examination
clarifies the underlying principles of these metrics and can serve as a
reference or introduction to scene graph metrics.
Furthermore, to facilitate the usage of these metrics, we introduce a
standalone Python package called SGBench that efficiently implements all
defined metrics, ensuring their accessibility to the research community.
Additionally, we present a scene graph benchmarking web service, that enables
researchers to compare scene graph generation methods and increase visibility
of new methods in a central place.
All of our code can be found at this https URL

本文提供了对场景图生成模型常用评价指标的细致定义和全面回顾，并介绍了一个名为 SGBench 的独立 Python 包，该包高效实现了所有定义的评价指标，以确保研究社区的可访问性，并展示了一个场景图基准测试网络服务，使研究人员能够比较场景图生成方法并集中展示新方法。