Action Scene Graphs for Long-Form Understanding of Egocentric Videos

• URL: http://arxiv.org/abs/2312.03391v1
• Date: Wed, 6 Dec 2023 10:01:43 GMT
• Title: Action Scene Graphs for Long-Form Understanding of Egocentric Videos
• Authors: Ivan Rodin, Antonino Furnari, Kyle Min, Subarna Tripathi, Giovanni Maria Farinella
• Abstract summary: We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos. EASGs provide a temporally evolving graph-based description of the actions performed by the camera wearer. We will release the dataset and the code to replicate experiments and annotations.
• Score: 23.058999979457546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 by adding manually labeled Egocentric Action Scene Graphs offering a rich set of annotations designed 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, egocentric action anticipation and egocentric activity summarization, highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and the code to replicate experiments and annotations.

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