English(EN) Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

新框架S4ECA提升遥感图像分割效率

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-29 12:54

研究人员推出了一种名为S4ECA的新框架，以提高指代遥感图像分割（RRSIS）的效率和准确性。该方法解决了在小型数据集上对大型基础模型进行完全微调所带来的计算强度和潜在的泛化问题。S4ECA采用双编码器适配器架构，实现参数高效适应，仅更新2.4%的骨干网络参数。该框架通过有效对齐跨模态信息和动态强调相关的视觉上下文，在RRSIS-D和RefSegRS数据集上取得了最先进的性能。 AI

影响这项研究为训练遥感图像分割模型提供了一种更有效的方法，有望降低计算成本并提高在专业数据集上的性能。

排序理由该集群描述了一篇关于特定AI任务新框架的详细研究论文。

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报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Kun Li, Shengxi Gui, Francesco Nex, Michael Ying Yang · 2026-06-30 04:00

Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

arXiv:2606.30244v1 Announce Type: new Abstract: Referring Remote Sensing Image Segmentation (RRSIS) seeks to localize and segment the target object or region specified by a natural language expression in a remote sensing image. While existing RRSIS models have benefited from larg…
arXiv cs.CV TIER_1 English(EN) · Michael Ying Yang · 2026-06-29 12:54

Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

Referring Remote Sensing Image Segmentation (RRSIS) seeks to localize and segment the target object or region specified by a natural language expression in a remote sensing image. While existing RRSIS models have benefited from large-scale foundation models, they predominantly re…

报道来源 [2]

Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

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