Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos

Abstract

We propose a self-supervised method for learning repre- sentations based on spatial audio-visual correspondences in egocentric videos. Our method uses a masked auto- encoding framework to synthesize masked binaural audio through the synergy of audio and vision, thereby learning useful spatial relationships between the two modalities We use our pretrained features to tackle two downstream video tasks requiring spatial understanding in social scenar- ios: active speaker detection and spatial audio denoising. Through extensive experiments, we show that our features are generic enough to improve over multiple state-of-the- art baselines on both tasks on two challenging egocentric video datasets that offer binaural audio, EgoCom and Easy-Com. Project: http://vision.cs.utexas.edu/projects/ego_av_corr.