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In egocentric videos, actions occur in quick succession. We capitalise on theaction's temporal context and propose a method that learns to attend tosurrounding actions in order to improve recognition performance. To incorporatethe temporal context, we propose a transformer-based multimodal model thatingests video and audio as input modalities, with an explicit language modelproviding action sequence context to enhance the predictions. We test ourapproach on EPIC-KITCHENS and EGTEA datasets reporting state-of-the-artperformance. Our ablations showcase the advantage of utilising temporal contextas well as incorporating audio input modality and language model to rescorepredictions. Code and models at: https://github.com/ekazakos/MTCN.

With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition