OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 43 of its 57 references that are in the map. First posted on arXiv 10 Feb 2022.
Builds on
43 of 57 references.
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Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 1.
4 papers.
- 2024Exploring Missing Modality in Multimodal Egocentric Datasetscited by 7
- 2024Test-Time Adaptation for Combating Missing Modalities in Egocentric Videoscited by 2
- 2023Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localizationcited by 1
- 2025DEL: Dense Event Localization for Multi-modal Audio-Visual Understandingcited by 1
- 2025DEL: Dense Event Localization for Multi-modal Audio-Visual Understanding29 Jun 2025
- 2024Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos23 Apr 2024
- 2024Exploring Missing Modality in Multimodal Egocentric Datasets21 Jan 2024
- 2023Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization5 Oct 2023