  We propose to explore a new problem called audio-visual segmentation (AVS),
in which the goal is to output a pixel-level map of the object(s) that produce
sound at the time of the image frame. To facilitate this research, we construct
the first audio-visual segmentation benchmark (AVSBench), providing pixel-wise
annotations for the sounding objects in audible videos. Two settings are
studied with this benchmark: 1) semi-supervised audio-visual segmentation with
a single sound source and 2) fully-supervised audio-visual segmentation with
multiple sound sources. To deal with the AVS problem, we propose a novel method
that uses a temporal pixel-wise audio-visual interaction module to inject audio
semantics as guidance for the visual segmentation process. We also design a
regularization loss to encourage the audio-visual mapping during training.
Quantitative and qualitative experiments on the AVSBench compare our approach
to several existing methods from related tasks, demonstrating that the proposed
method is promising for building a bridge between the audio and pixel-wise
visual semantics. Code is available at https://github.com/OpenNLPLab/AVSBench.
