  Action anticipation in egocentric videos is a difficult task due to the
inherently multi-modal nature of human actions. Additionally, some actions
happen faster or slower than others depending on the actor or surrounding
context which could vary each time and lead to different predictions. Based on
this idea, we build upon RULSTM architecture, which is specifically designed
for anticipating human actions, and propose a novel attention-based technique
to evaluate, simultaneously, slow and fast features extracted from three
different modalities, namely RGB, optical flow, and extracted objects. Two
branches process information at different time scales, i.e., frame-rates, and
several fusion schemes are considered to improve prediction accuracy. We
perform extensive experiments on EpicKitchens-55 and EGTEA Gaze+ datasets, and
demonstrate that our technique systematically improves the results of RULSTM
architecture for Top-5 accuracy metric at different anticipation times.
