English(EN) Multimodal Foundation Models Adaptation based on Domain-Aware Relaxed Orthogonal Subspace for Remote Sensing

新的DROS方法增强了基础模型在遥感领域的适应性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种名为域感知松弛正交子空间适应（DROS）的新方法，以提高微调大型基础模型在遥感任务中的效率。该方法解决了“子空间不匹配”问题，即固定的适应子空间与遥感特定数据分布不匹配。DROS将低秩适应重新构建为数据条件子空间学习，利用下游数据的激活统计信息来指导子空间初始化和灵活的几何变换。一项名为MM-DROS的扩展通过实现多模态设置中的高效跨模态交互，进一步增强了这一能力。实验表明，DROS在不增加推理成本的情况下，实现了最先进的性能，甚至优于完全微调。 AI

影响提高了基础模型在遥感等专业领域的效率和性能。

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Han Luo, Ruoyu Yang, Yinhe Liu, Yanfei Zhong · 2026-09-15 04:00

面向遥感的多模态基础模型基于域感知松弛正交子空间进行适应

arXiv:2609.13654v1 Announce Type: new Abstract: Pretrained foundation models (FMs) have achieved remarkable success in computer vision, yet their high fine-tuning cost limits practical deployment. Parameter-efficient fine-tuning (PEFT) methods such as Low-Rank Adaptation (LoRA) i…

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面向遥感的多模态基础模型基于域感知松弛正交子空间进行适应

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