Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery
  Based on Large Vision Models
Xiyu Qi Yifan Wu Yongqiang Mao Wenhui Zhang Yidan Zhang
The Segment Anything Model (SAM) exhibits remarkable versatility and
zero-shot learning abilities, owing largely to its extensive training data
(SA-1B). Recognizing SAM's dependency on manual guidance given its
category-agnostic nature, we identified unexplored potential within few-shot
semantic segmentation tasks for remote sensing imagery. This research
introduces a structured framework designed for the automation of few-shot
semantic segmentation. It utilizes the SAM model and facilitates a more
efficient generation of semantically discernible segmentation outcomes. Central
to our methodology is a novel automatic prompt learning approach, leveraging
prior guided masks to produce coarse pixel-wise prompts for SAM. Extensive
experiments on the DLRSD datasets underline the superiority of our approach,
outperforming other available few-shot methodologies.