  In this report we describe the technical details of our submission to the
EPIC-Kitchens Action Recognition 2020 Challenge. To participate in the
challenge we deployed spatio-temporal feature extraction and aggregation models
we have developed recently: Gate-Shift Module (GSM) [1] and EgoACO, an
extension of Long Short-Term Attention (LSTA) [2]. We design an ensemble of GSM
and EgoACO model families with different backbones and pre-training to generate
the prediction scores. Our submission, visible on the public leaderboard with
team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 40.0% on
S1 setting, and 25.71% on S2 setting, using only RGB.
