In egocentric videos, actions occur in quick succession. We capitalise on the action's temporal context and propose a method that learns to attend to surrounding actions in order to improve recognition performance. To incorporate the temporal context, we propose a transformer-based multimodal model that ingests video and audio as input modalities, with an explicit language model providing action sequence context to enhance the predictions. We test our approach on EPIC-KITCHENS and EGTEA datasets reporting state-of-the-art performance. Our ablations showcase the advantage of utilising temporal context as well as incorporating audio input modality and language model to rescore predictions. Code and models at: https://github.com/ekazakos/MTCN.
@article{arxiv.2111.01024,
title = {With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition},
author = {Evangelos Kazakos and Jaesung Huh and Arsha Nagrani and Andrew Zisserman and Dima Damen},
journal= {arXiv preprint arXiv:2111.01024},
year = {2021}
}