  In this paper we propose a new framework to categorize social interactions in
egocentric videos, we named InteractionGCN. Our method extracts patterns of
relational and non-relational cues at the frame level and uses them to build a
relational graph from which the interactional context at the frame level is
estimated via a Graph Convolutional Network based approach. Then it propagates
this context over time, together with first-person motion information, through
a Gated Recurrent Unit architecture. Ablation studies and experimental
evaluation on two publicly available datasets validate the proposed approach
and establish state of the art results.
