  This paper proposes an interaction reasoning network for modelling
spatio-temporal relationships between hands and objects in video. The proposed
interaction unit utilises a Transformer module to reason about each acting
hand, and its spatio-temporal relation to the other hand as well as objects
being interacted with. We show that modelling two-handed interactions are
critical for action recognition in egocentric video, and demonstrate that by
using positionally-encoded trajectories, the network can better recognise
observed interactions. We evaluate our proposal on EPIC-KITCHENS and
Something-Else datasets, with an ablation study.
