SoundingActions: Learning How Actions Sound from Narrated Egocentric
  Videos
Changan Chen Kumar Ashutosh Rohit Girdhar David Harwath Kristen Grauman
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.