  The primary aim of Audio-Visual Segmentation (AVS) is to precisely identify
and locate auditory elements within visual scenes by accurately predicting
segmentation masks at the pixel level. Achieving this involves comprehensively
considering data and model aspects to address this task effectively. This study
presents a lightweight approach, SAVE, which efficiently adapts the pre-trained
segment anything model (SAM) to the AVS task. By incorporating an image encoder
adapter into the transformer blocks to better capture the distinct dataset
information and proposing a residual audio encoder adapter to encode the audio
features as a sparse prompt, our proposed model achieves effective audio-visual
fusion and interaction during the encoding stage. Our proposed method
accelerates the training and inference speed by reducing the input resolution
from 1024 to 256 pixels while achieving higher performance compared with the
previous SOTA. Extensive experimentation validates our approach, demonstrating
that our proposed model outperforms other SOTA methods significantly. Moreover,
leveraging the pre-trained model on synthetic data enhances performance on real
AVSBench data, achieving 84.59 mIoU on the S4 (V1S) subset and 70.28 mIoU on
the MS3 (V1M) set with only 256 pixels for input images. This increases up to
86.16 mIoU on the S4 (V1S) and 70.83 mIoU on the MS3 (V1M) with inputs of 1024
pixels.
