Egocentric Action Recognition by Video Attention and Temporal Context (2007.01883v1)

Abstract: We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem of simultaneously predicting a single verb' andnoun' class label given an input trimmed video clip. That is, a verb' and anoun' together define a compositional action' class. The challenging aspects of this real-life action recognition task include small fast moving objects, complex hand-object interactions, and occlusions. At the core of our submission is a recently-proposed spatial-temporal video attention model, calledW3' (What-Where-When') attention~\cite{perez2020knowing}. We further introduce a simple yet effective contextual learning mechanism to modelaction' class scores directly from long-term temporal behaviour based on the verb' andnoun' prediction scores. Our solution achieves strong performance on the challenge metrics without using object-specific reasoning nor extra training data. In particular, our best solution with multimodal ensemble achieves the 2$^{nd}$ best position for verb', and 3$^{rd}$ best fornoun' and `action' on the Seen Kitchens test set.