SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Chen, Changan, Ashutosh, Kumar, Girdhar, Rohit, Harwath, David, Grauman, Kristen
Format: Preprint
Veröffentlicht: 2024
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866913305225854976
author Chen, Changan
Ashutosh, Kumar
Girdhar, Rohit
Harwath, David
Grauman, Kristen
author_facet Chen, Changan
Ashutosh, Kumar
Girdhar, Rohit
Harwath, David
Grauman, Kristen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2404_05206
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos
Chen, Changan
Ashutosh, Kumar
Girdhar, Rohit
Harwath, David
Grauman, Kristen
Computer Vision and Pattern Recognition
Multimedia
Sound
Audio and Speech Processing
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.
title SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos
topic Computer Vision and Pattern Recognition
Multimedia
Sound
Audio and Speech Processing
url https://arxiv.org/abs/2404.05206