Fantastic Animals and Where to Find Them: Segment Any Marine Animal with
  Dual SAM
Pingping Zhang Tianyu Yan Yang Liu Huchuan Lu
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.