Papers › With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition
Evangelos Kazakos, Jaesung Huh, Arsha Nagrani, Andrew Zisserman, Dima Damen
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.
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