# Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models Source: [Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:02.477Z Authors: Xiyu Qi, Yifan Wu, Yongqiang Mao, Wenhui Zhang, Yidan Zhang Published: 2023-11-22T07:07:55.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()