mlsanityData to 7 Oct 2026

SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos

arXiv 2404.05206 ↗First posted 8 Apr 2024cs.CV cs.MM cs.SDcited by 11

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Abstract (arXiv)

We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos. Whereas existing methods rely on curated data with known audio-visual correspondence, our multimodal contrastive-consensus coding (MC3) embedding reinforces the associations between audio, language, and vision when all modality pairs agree, while diminishing those associations when any one pair does not. We show our approach can successfully discover how the long tail of human actions sound from egocentric video, outperforming an array of recent multimodal embedding techniques on two datasets (Ego4D and EPIC-Sounds) and multiple cross-modal tasks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 44 of its 57 references that are in the map. First posted on arXiv 8 Apr 2024.

Builds on

44 of 57 references.

References · 44

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32 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 1.

11 papers.