基于平流优化的遥感图像无训练实体级小样本分割
Training-Free Entity-Level Few-Shot Segmentation of Remote Sensing Images with Advection Refinement
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中文总结 AI 辅助
该研究针对现有跨域小样本分割方法训练成本高、预测结果碎片化的问题，提出一种基于平流优化的无训练实体级遥感图像小样本分割框架，可提升 SAM3 的相关适应能力。
中文摘要 AI 辅助
现有跨域小样本分割方法因源域 episodic 训练和逐像素密集预测而存在高训练成本问题，且常产生碎片化、含噪声的预测结果。为解决这些问题，我们提出一种用于遥感图像的无训练实体级小样本分割框架，采用平流优化机制。具体而言，我们首先利用 SAM3 的通用几何先验生成类别无关的实体基元；通过将小样本推理从像素级预测重新表述为实体级推理，构建前景与背景原型，并结合 SAM3 输出的密集文本语义响应，构建多模态语义势场；此外，引入基于平流方程的语义优化机制，在特征空间和相似度空间中传播类别感知信息，增强语义连续性并抑制局部纹理噪声。在多个遥感数据集上开展的大量实验表明，所提框架可有效缓解域偏移与局部噪声，在无需额外训练的情况下大幅提升 SAM3 对遥感小样本分割的适应能力，我们的代码将在该 https URL 公开。
英文摘要
Existing cross-domain few-shot segmentation approaches suffer from high training costs due to source-domain episodic training and pixel-wise dense prediction, while often producing fragmented and noisy predictions. To overcome these issues, we propose a training-free entity-level few-shot segmentation framework for remote sensing images with advection refinement. Specifically, we first leverage SAM3's generic geometric priors to generate category-agnostic entity primitives. By reformulating few-shot inference from pixel-level prediction to entity-level reasoning, foreground and background prototypes are constructed and combined with dense textual semantic responses from SAM3 to build a multi-modal semantic potential field. Furthermore, an advection equation-based semantic refinement mechanism is introduced to propagate category-aware information across both feature and similarity spaces, enhancing semantic continuity and suppressing local texture noise. Extensive experiments on multiple remote sensing datasets demonstrate that the proposed framework effectively mitigates domain shift and local noise, substantially improving SAM3's adaptation capability for remote sensing few-shot segmentation without additional training. Our code will be publicly available at https://github.com/yu-ni1989/ELFSS-AR.
