Weakly-Supervised Audio-Visual Segmentation
Main Conference Track
Authors: Shentong Mo, Bhiksha Raj

Audio-visual segmentation is a challenging task that aims to predict pixel-level masks for sound sources in a video.  Previous work applied a comprehensive manually designed architecture with countless pixel-wise accurate masks as supervision.  However, these pixel-level masks are expensive and not available in all cases.  In this work, we aim to simplify the supervision as the instance-level annotation, $\textit{i.e.}$, weakly-supervised audio-visual segmentation.  We present a novel Weakly-Supervised Audio-Visual Segmentation framework, namely WS-AVS, that can learn multi-scale audio-visual alignment with multi-scale multiple-instance contrastive learning for audio-visual segmentation.  Extensive experiments on AVSBench demonstrate the effectiveness of our WS-AVS in the weakly-supervised audio-visual segmentation of single-source and multi-source scenarios.