English(EN) SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection

新的SCL方法增强了遥感变化检测的泛化能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种名为SCL的新方法，用于遥感图像的变化检测，旨在提高跨数据集的泛化能力。该方法利用单时相多模态对比学习策略，并结合了视觉-语言预训练模型。SCL通过在单时相图像上进行训练，无需目标数据集特定的训练，解决了对大量配对标注数据的需求，并在性能上优于现有方法。 AI

影响增强了遥感变化检测的泛化能力，可能降低数据标注成本。

排序理由该集群包含一篇详细介绍特定AI任务新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Qiangang Du, Jinlong Peng, Xu Chen, Qingdong He, Liren He, Qiang Nie, Mingmin Chi · 2026-06-02 04:00

SCL：通过单时相多模态对比学习实现遥感变化检测的领域泛化

arXiv:2404.11326v5 Announce Type: replace Abstract: In recent years, change detection and anomaly detection models based on CNN and transformer have achieved remarkable success across various datasets based on paired data. However, most such methods exhibit limited crossdataset g…