Abstract
Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data regimes, such as few-shot learning, remains insufficiently explored. In this work, we present the first structured benchmark for evaluating few-shot adaptation methods on RSVLMs. We conduct comprehensive experiments across ten remote sensing scene classification datasets, applying five widely used few-shot adaptation strategies to three state-of-the-art RSVLMs with varying backbones. Our findings reveal that models with similar zero-shot performance can exhibit markedly different behavior under few-shot adaptation, with some RSVLMs being inherently more amenable to such adaptation than others. The variability of performance and the absence of a clear winner among existing methods highlight the need for the development of more robust methods for few-shot adaptation tailored to RS. To facilitate future research, we provide a reproducible benchmarking framework and open-source code to systematically evaluate RSVLMs under few-shot conditions. The source code is publicly available on Github: this https URL
Abstract (translated)
远程感知视觉-语言模型（RSVLM）由于大规模预训练，在各种任务中展现了显著潜力，并实现了强大的零样本性能。然而，它们在低数据环境下（如少样本学习）的泛化能力仍需进一步探索。在这项工作中，我们首次提出了一个结构化的基准测试框架，用于评估针对远程感知视觉-语言模型的少样本适应方法。我们在十个不同的遥感场景分类数据集上进行了全面实验，将五种广泛使用的少样本适应策略应用于三种具有不同骨干网络的最先进的RSVLM。 我们的研究发现表明，即使零样本性能相似的模型，在进行少样本适应时也会表现出明显不同的行为，并且某些RSVLM比其他模型更适合这种适应。现有方法的表现差异性及缺乏明确的领先者强调了为远程感知开发更稳健的少样本适应方法的需求。 为了促进未来的研究，我们提供了一个可重复的基准测试框架和开源代码，以系统地评估在少样本条件下工作的RSVLM。源码可以在GitHub上公开获取：[此链接](https://github.com/remote-sensing-benchmark/few-shot-rs-adapter)
URL
https://arxiv.org/abs/2510.07135
