  Our interaction with the world is an inherently multimodal experience.
However, the understanding of human-to-object interactions has historically
been addressed focusing on a single modality. In particular, a limited number
of works have considered to integrate the visual and audio modalities for this
purpose. In this work, we propose a multimodal approach for egocentric action
recognition in a kitchen environment that relies on audio and visual
information. Our model combines a sparse temporal sampling strategy with a late
fusion of audio, spatial, and temporal streams. Experimental results on the
EPIC-Kitchens dataset show that multimodal integration leads to better
performance than unimodal approaches. In particular, we achieved a 5.18%
improvement over the state of the art on verb classification.
