ClassWise-SAM-Adapter: Parameter-Efficient Fine-Tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation

In the realm of artificial intelligence, the emergence of foundation models, backed by high computing capabilities and extensive data, has been revolutionary. A segment anything model (SAM), built on the vision transformer (ViT) model with millions of parameters and trained on its corresponding large-scale dataset SA-1B, excels in various segmentation scenarios relying on its significance of semantic information and generalization ability. Such achievement of visual foundation model stimulates continuous researches on specific downstream tasks in computer vision. The classwise-SAM-adapter (CWSAM) is designed to adapt the high-performing SAM for landcover classification on space-borne synthetic aperture radar…

• NN
  National Natural Science Foundation of China

  Awards: 61991422, 61991421