Action Scene Graphs for Long-Form Understanding of Egocentric Videos

Abstract

We present Egocentric Action Scene Graphs (EASGs) a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos such as verb-noun action labels by providing a temporally evolving graph-based description of the actions performed by the camera wearer including interacted objects their relationships and how actions unfold in time. Through a novel annotation procedure we extend the Ego4D dataset adding manually labeled Egocentric Action Scene Graphs which offer a rich set of annotations for long-from egocentric video understanding. We hence define the EASG generation task and provide a baseline approach establishing preliminary benchmarks. Experiments on two downstream tasks action anticipation and activity summarization highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and code to replicate experiments and annotations.