As an important pillar of underwater intelligence, Marine Animal Segmentation
(MAS) involves segmenting animals within marine environments. Previous methods
don't excel in extracting long-range contextual features and overlook the
connectivity between discrete pixels. Recently, Segment Anything Model (SAM)
offers a universal framework for general segmentation tasks. Unfortunately,
trained with natural images, SAM does not obtain the prior knowledge from
marine images. In addition, the single-position prompt of SAM is very
insufficient for prior guidance. To address these issues, we propose a novel
feature learning framework, named Dual-SAM for high-performance MAS. To this
end, we first introduce a dual structure with SAM's paradigm to enhance feature
learning of marine images. Then, we propose a Multi-level Coupled Prompt (MCP)
strategy to instruct comprehensive underwater prior information, and enhance
the multi-level features of SAM's encoder with adapters. Subsequently, we
design a Dilated Fusion Attention Module (DFAM) to progressively integrate
multi-level features from SAM's encoder. Finally, instead of directly
predicting the masks of marine animals, we propose a Criss-Cross Connectivity
Prediction (C$^3$P) paradigm to capture the inter-connectivity between discrete
pixels. With dual decoders, it generates pseudo-labels and achieves mutual
supervision for complementary feature representations, resulting in
considerable improvements over previous techniques. Extensive experiments
verify that our proposed method achieves state-of-the-art performances on five
widely-used MAS datasets. The code is available at
this https URL

提出了一个名为 Dual-SAM 的新型特征学习框架，结合 Segment Anything Model (SAM) 的范例，通过多层次的耦合提示策略 (MCP) 和交叉连接预测范式 (C^3P) 来增强对海洋动物图像的特征学习和分割，提供了在海洋动物分割任务中新的高性能解决方案。