Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery
Based on Large Vision Models
- URL: http://arxiv.org/abs/2311.13200v1
- Date: Wed, 22 Nov 2023 07:07:55 GMT
- Title: Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery
Based on Large Vision Models
- Authors: Xiyu Qi, Yifan Wu, Yongqiang Mao, Wenhui Zhang, Yidan Zhang
- Abstract summary: 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.
- Score: 14.292149307183967
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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