Audio and visual signals typically occur simultaneously, and humans possess
an innate ability to correlate and synchronize information from these two
modalities. Recently, a challenging problem known as Audio-Visual Segmentation
(AVS) has emerged, intending to produce segmentation maps for sounding objects
within a scene. However, the methods proposed so far have not sufficiently
integrated audio and visual information, and the computational costs have been
extremely high. Additionally, the outputs of different stages have not been
fully utilized. To facilitate this research, we introduce a novel Progressive
Confident Masking Attention Network (PMCANet). It leverages attention
mechanisms to uncover the intrinsic correlations between audio signals and
visual frames. Furthermore, we design an efficient and effective
cross-attention module to enhance semantic perception by selecting query
tokens. This selection is determined through confidence-driven units based on
the network's multi-stage predictive outputs. Experiments demonstrate that our
network outperforms other AVS methods while requiring less computational
resources.

通过引入渐进自信掩蔽注意力网络（PMCANet），利用注意机制揭示音频信号和视觉帧之间的内在相关性，并设计了高效且有效的跨注意模块来通过选择查询标记增强语义感知。实验证明，我们的网络在需要更少的计算资源的情况下比其他 AVS 方法表现更好。