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.

翻译：近期，分割一切模型（SAM）在生成高质量对象掩码和实现零样本图像分割方面展现出卓越性能。然而，作为通用视觉模型，SAM主要在大规模自然光图像上进行训练。在水下场景中，由于光线散射和吸收，其性能出现显著下降。同时，SAM解码器的简洁性可能导致细粒度物体细节的丢失。针对上述问题，我们提出了一种名为MAS-SAM的新型特征学习框架用于海洋动物分割，该框架通过向SAM编码器集成有效适配器并构建金字塔形解码器来实现。具体而言，我们首先为水下场景构建了集成有效适配器的新SAM编码器。随后引入超图提取模块（HEM）生成多尺度特征以提供全面引导。最后提出渐进预测解码器（PPD）汇聚多尺度特征并预测最终分割结果。当与融合注意力模块（FAM）结合时，我们的方法能够从全局上下文线索到细粒度局部细节中提取更丰富的海洋信息。在四个公开MAS数据集上的大量实验表明，我们的MAS-SAM相比其他典型分割方法可获得更优结果。源代码已开源至https://github.com/Drchip61/MAS-SAM。