Adapting Segment-Anything Model to Diverse Downstream Segmentation Tasks via Weakly Supervised Self-Training

The authors propose a weakly supervised self-training approach to adapt the pre-trained Segment-Anything (SAM) model to diverse downstream segmentation tasks, overcoming the generalization issues of SAM under significant distribution shift.