  Segment Anything Model (SAM) has recently shown its powerful effectiveness in
visual segmentation tasks. However, there is less exploration concerning how
SAM works on audio-visual tasks, such as visual sound localization and
segmentation. In this work, we propose a simple yet effective audio-visual
localization and segmentation framework based on the Segment Anything Model,
namely AV-SAM, that can generate sounding object masks corresponding to the
audio. Specifically, our AV-SAM simply leverages pixel-wise audio-visual fusion
across audio features and visual features from the pre-trained image encoder in
SAM to aggregate cross-modal representations. Then, the aggregated cross-modal
features are fed into the prompt encoder and mask decoder to generate the final
audio-visual segmentation masks. We conduct extensive experiments on
Flickr-SoundNet and AVSBench datasets. The results demonstrate that the
proposed AV-SAM can achieve competitive performance on sounding object
localization and segmentation.
