  Audio-visual video segmentation~(AVVS) aims to generate pixel-level maps of
sound-producing objects within image frames and ensure the maps faithfully
adhere to the given audio, such as identifying and segmenting a singing person
in a video. However, existing methods exhibit two limitations: 1) they address
video temporal features and audio-visual interactive features separately,
disregarding the inherent spatial-temporal dependence of combined audio and
video, and 2) they inadequately introduce audio constraints and object-level
information during the decoding stage, resulting in segmentation outcomes that
fail to comply with audio directives. To tackle these issues, we propose a
decoupled audio-video transformer that combines audio and video features from
their respective temporal and spatial dimensions, capturing their combined
dependence. To optimize memory consumption, we design a block, which, when
stacked, enables capturing audio-visual fine-grained combinatorial-dependence
in a memory-efficient manner. Additionally, we introduce audio-constrained
queries during the decoding phase. These queries contain rich object-level
information, ensuring the decoded mask adheres to the sounds. Experimental
results confirm our approach's effectiveness, with our framework achieving a
new SOTA performance on all three datasets using two backbones. The code is
available at \url{https://github.com/aspirinone/CATR.github.io}
