AI RESEARCH
ReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images
arXiv CS.CV
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ArXi:2511.21606v4 Announce Type: replace Interactive segmentation models such as the Segment Anything Model (SAM) have nstrated remarkable generalization on natural images, but they perform suboptimally on remote sensing imagery (RSI) due to severe domain shifts and the scarcity of dense annotations. To address this limitation, we propose a point-supervised, self-prompting framework that adapts SAM to RSI using only sparse point annotations.