AquaSAM: Underwater Image Foreground Segmentation

Understand

The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted.

• This work presents AquaSAM, the first attempt to extend the success of SAM on underwater images with the purpose of creating a versatile method for the segmentation of various underwater targets.
• To achieve this, we begin by classifying and extracting various labels automatically in SUIM dataset.
• Subsequently, we develop a straightforward fine-tuning method to adapt SAM to general foreground underwater image segmentation.