  With the breakthrough of large models, Segment Anything Model (SAM) and its
extensions have been attempted to apply in diverse tasks of computer vision.
Underwater salient instance segmentation is a foundational and vital step for
various underwater vision tasks, which often suffer from low segmentation
accuracy due to the complex underwater circumstances and the adaptive ability
of models. Moreover, the lack of large-scale datasets with pixel-level salient
instance annotations has impeded the development of machine learning techniques
in this field. To address these issues, we construct the first large-scale
underwater salient instance segmentation dataset (USIS10K), which contains
10,632 underwater images with pixel-level annotations in 7 categories from
various underwater scenes. Then, we propose an Underwater Salient Instance
Segmentation architecture based on Segment Anything Model (USIS-SAM)
specifically for the underwater domain. We devise an Underwater Adaptive Visual
Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts
into the segmentation network. We further design an out-of-the-box underwater
Salient Feature Prompter Generator (SFPG) to automatically generate salient
prompters instead of explicitly providing foreground points or boxes as prompts
in SAM. Comprehensive experimental results show that our USIS-SAM method can
achieve superior performance on USIS10K datasets compared to the
state-of-the-art methods. Datasets and codes are released on
https://github.com/LiamLian0727/USIS10K.
