A Review and Efficient Implementation of Scene Graph Generation Metrics

Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2567-2575

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 under https://lorjul.github.io/sgbench/.