  Recently, Segment Anything Model (SAM) shows exceptional performance in
generating high-quality object masks and achieving zero-shot image
segmentation. However, as a versatile vision model, SAM is primarily trained
with large-scale natural light images. In underwater scenes, it exhibits
substantial performance degradation due to the light scattering and absorption.
Meanwhile, the simplicity of the SAM's decoder might lead to the loss of
fine-grained object details. To address the above issues, we propose a novel
feature learning framework named MAS-SAM for marine animal segmentation, which
involves integrating effective adapters into the SAM's encoder and constructing
a pyramidal decoder. More specifically, we first build a new SAM's encoder with
effective adapters for underwater scenes. Then, we introduce a Hypermap
Extraction Module (HEM) to generate multi-scale features for a comprehensive
guidance. Finally, we propose a Progressive Prediction Decoder (PPD) to
aggregate the multi-scale features and predict the final segmentation results.
When grafting with the Fusion Attention Module (FAM), our method enables to
extract richer marine information from global contextual cues to fine-grained
local details. Extensive experiments on four public MAS datasets demonstrate
that our MAS-SAM can obtain better results than other typical segmentation
methods. The source code is available at https://github.com/Drchip61/MAS-SAM.
