SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos

• URL: http://arxiv.org/abs/2404.05206v1
• Date: Mon, 8 Apr 2024 05:19:28 GMT
• Title: SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos
• Authors: Changan Chen, Kumar Ashutosh, Rohit Girdhar, David Harwath, Kristen Grauman,
• Abstract summary: We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos. Our multimodal contrastive-consensus coding (MC3) embedding reinforces the associations between audio, language, and vision when all modality pairs agree.
• Score: 77.55518265996312
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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