  Recently, transformer-based models have demonstrated remarkable performance
on audio-visual segmentation (AVS) tasks. However, their expensive
computational cost makes real-time inference impractical. By characterizing
attention maps of the network, we identify two key obstacles in AVS models: 1)
attention dissipation, corresponding to the over-concentrated attention weights
by Softmax within restricted frames, and 2) inefficient, burdensome transformer
decoder, caused by narrow focus patterns in early stages. In this paper, we
introduce AVESFormer, the first real-time Audio-Visual Efficient Segmentation
transformer that achieves fast, efficient and light-weight simultaneously. Our
model leverages an efficient prompt query generator to correct the behaviour of
cross-attention. Additionally, we propose ELF decoder to bring greater
efficiency by facilitating convolutions suitable for local features to reduce
computational burdens. Extensive experiments demonstrate that our AVESFormer
significantly enhances model performance, achieving 79.9% on S4, 57.9% on MS3
and 31.2% on AVSS, outperforming previous state-of-the-art and achieving an
excellent trade-off between performance and speed. Code can be found at
https://github.com/MarkXCloud/AVESFormer.git.
