  Sound source localization is a typical and challenging task that predicts the
location of sound sources in a video. Previous single-source methods mainly
used the audio-visual association as clues to localize sounding objects in each
image. Due to the mixed property of multiple sound sources in the original
space, there exist rare multi-source approaches to localizing multiple sources
simultaneously, except for one recent work using a contrastive random walk in
the graph with images and separated sound as nodes. Despite their promising
performance, they can only handle a fixed number of sources, and they cannot
learn compact class-aware representations for individual sources. To alleviate
this shortcoming, in this paper, we propose a novel audio-visual grouping
network, namely AVGN, that can directly learn category-wise semantic features
for each source from the input audio mixture and image to localize multiple
sources simultaneously. Specifically, our AVGN leverages learnable audio-visual
class tokens to aggregate class-aware source features. Then, the aggregated
semantic features for each source can be used as guidance to localize the
corresponding visual regions. Compared to existing multi-source methods, our
new framework can localize a flexible number of sources and disentangle
category-aware audio-visual representations for individual sound sources. We
conduct extensive experiments on MUSIC, VGGSound-Instruments, and VGG-Sound
Sources benchmarks. The results demonstrate that the proposed AVGN can achieve
state-of-the-art sounding object localization performance on both single-source
and multi-source scenarios. Code is available at
\url{https://github.com/stoneMo/AVGN}.
