  With breakthroughs in large-scale modeling, the Segment Anything Model (SAM)
and its extensions have been attempted for applications in various underwater
visualization tasks in marine sciences, and have had a significant impact on
the academic community. Recently, Meta has further developed the Segment
Anything Model 2 (SAM2), which significantly improves running speed and
segmentation accuracy compared to its predecessor. This report aims to explore
the potential of SAM2 in marine science by evaluating it on the underwater
instance segmentation benchmark datasets UIIS and USIS10K. The experiments show
that the performance of SAM2 is extremely dependent on the type of
user-provided prompts. When using the ground truth bounding box as prompt, SAM2
performed excellently in the underwater instance segmentation domain. However,
when running in automatic mode, SAM2's ability with point prompts to sense and
segment underwater instances is significantly degraded. It is hoped that this
paper will inspire researchers to further explore the SAM model family in the
underwater domain. The results and evaluation codes in this paper are available
at https://github.com/LiamLian0727/UnderwaterSAM2Eval.
