EPIC-fusion: audio-visual temporal binding for egocentric action recognition

Conference item

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: Kazakos, E; Nagrani, A; Zisserman, A; Damen, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Institute of Electrical and Electronics Engineers
• Host title: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
• Pages: 5491-5500
• Publication date: 2020-02-27
• Acceptance date: 2019-07-22
• Event title: 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
• Event location: Seoul, South Korea
• Event website: (http://iccv2019.thecvf.com/
• Event start date: 2019-10-27
• Event end date: 2019-11-02
• DOI: 10.1109/ICCV.2019.00559
• EISSN: 2380-7504
• ISSN: 1550-5499
• EISBN: 9781728148038
• ISBN: 9781728148045
• Language: English
• Keywords: fuses; training; feature extraction; visualization; Microsoft Windows; computer architecture; computer vision; FFR