Underwater Salient Instance Segmentation

Underwater salient instance segmentation aims to automatically identify and outline individual objects of interest within underwater images, a crucial step for various marine applications. Current research focuses on adapting and improving large-scale models like the Segment Anything Model (SAM) for this challenging environment, often incorporating specialized modules to handle the unique visual characteristics of underwater imagery, such as poor visibility and color distortion. These advancements, coupled with the development of larger, more comprehensive underwater datasets, are improving the accuracy and efficiency of underwater object segmentation, enabling more robust analysis of marine ecosystems and supporting advancements in autonomous underwater vehicles.