  Egocentric videos capture sequences of human activities from a first-person
perspective and can provide rich multimodal 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.
