A Review and Efficient Implementation of Scene Graph Generation Metrics
Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart
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 https://lorjul.github.io/sgbench/.