OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos

Merey Ramazanova, Victor Escorcia, Fabian Caba Heilbron, Chen Zhao, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Egocentric videos capture sequences of human activities from a first-person perspective and can provide rich multi-modal signals. However, most current localization methods use third-person videos and only incorporate visual information. In this work, we take a deep look into the effectiveness of audiovisual context in detecting actions in egocentric videos and introduce a simple-yet-effective approach via Observing, Watching, and Listening (OWL). OWL leverages audiovisual information and context for egocentric Temporal Action Localization (TAL). We validate our approach in two large-scale datasets, EPIC-KITCHENS and HOMAGE. Extensive experiments demonstrate the relevance of the audiovisual temporal context. Namely, we boost the localization performance (mAP) over visual-only models by +2.23% and +3.35% in the above datasets.
Original languageEnglish (US)
Title of host publication2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
StatePublished - Jun 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-08-21
Acknowledgements: This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding, and SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence (SDAIA-KAUST AI).


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