Year: 2019
Type: conference-paper
Abstract: We focus on multi-modal fusion for egocentric action recognition, and propose a novel architecture for multimodal temporal-binding, i.e. the combination of modalities within a range of temporal offsets. We train the architecture with three modalities - RGB, Flow and Audio - and combine them with mid-level fusion alongside sparse temporal sampling off used representations. In contrast with previous works, modalities are fused before temporal aggregation, with shared modality and fusion weights over time. Our proposed architecture is trained end-to-end, outperforming individual modalities as well as late-fusion of modalities. We demonstrate the importance of audio in egocentric vision, on per-class basis, for identifying actions as well as interacting objects. Our method achieves state of the art results on both the seen and unseen test sets of the largest egocentric dataset: EPIC-Kitchens, on all metrics using the public leaderboard.
Authors: Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, Dima Damen
Institutions: University of Bristol, University of Oxford
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2969613290