  Audio-Visual Segmentation (AVS) is a challenging task, which aims to segment
sounding objects in video frames by exploring audio signals. Generally AVS
faces two key challenges: (1) Audio signals inherently exhibit a high degree of
information density, as sounds produced by multiple objects are entangled
within the same audio stream; (2) Objects of the same category tend to produce
similar audio signals, making it difficult to distinguish between them and thus
leading to unclear segmentation results. Toward this end, we propose TransAVS,
the first Transformer-based end-to-end framework for AVS task. Specifically,
TransAVS disentangles the audio stream as audio queries, which will interact
with images and decode into segmentation masks with full transformer
architectures. This scheme not only promotes comprehensive audio-image
communication but also explicitly excavates instance cues encapsulated in the
scene. Meanwhile, to encourage these audio queries to capture distinctive
sounding objects instead of degrading to be homogeneous, we devise two
self-supervised loss functions at both query and mask levels, allowing the
model to capture distinctive features within similar audio data and achieve
more precise segmentation. Our experiments demonstrate that TransAVS achieves
state-of-the-art results on the AVSBench dataset, highlighting its
effectiveness in bridging the gap between audio and visual modalities.
