  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
https://github.com/Drchip61/Dual_SAM.
