PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Southwest Jiaotong University · Agency for Science, Technology and Research · +1 more institution
Abstract
Segment anything model (SAM) is an advanced foundational model for image segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the domain gap between RSIs and natural images, traditional methods typically use SAM as a source pretrained model and fine-tune it with fully supervised masks. Unlike these methods, our work focuses on fine-tuning SAM using more convenient and challenging point annotations. Leveraging SAM’s zero-shot capability, we adopt a self-training framework that iteratively generates pseudolabels. However, noisy labels in pseudolabels can cause error accumulation. To address this, we introduce prototype-based regularization (PBR), where target prototypes are…
Citation impact
- FWCI
- 38.72
- Percentile
- 100%
- References
- 81
Authors
5Topics & keywords
- Remote sensing
- Computer science
- Computer vision
- Artificial intelligence
- Geology