Abstract
In recent years, large-scale vision-language models (VLMs) like CLIP have gained attention for their zero-shot inference using instructional text prompts. While these models excel in general computer vision, their potential for domain generalization in remote sensing (RS) remains underexplored. Existing approaches enhance prompt learning by generating visual prompt tokens but rely on full-image features, introducing noise and background artifacts that vary within a class, causing misclassification. To address this, we propose FrogDogNet, a novel prompt learning framework integrating Fourier frequency filtering and self-attention to improve RS scene classification and domain generalization. FrogDogNet selectively retains invariant low-frequency components while eliminating noise and irrelevant backgrounds, ensuring robust feature representation across domains. The model first extracts significant features via projection and self-attention, then applies frequency-based filtering to preserve essential structural information for prompt learning. Extensive experiments on four RS datasets and three domain generalization tasks show that FrogDogNet consistently outperforms state-of-the-art prompt learning methods, demonstrating superior adaptability across domain shifts. Our findings highlight the effectiveness of frequency-based invariant feature retention in generalization, paving the way for broader applications. Our code is available at this https URL
Abstract (translated)
近年来，大型视觉-语言模型（VLM），如CLIP，因其能够使用指令文本提示进行零样本推理而备受关注。尽管这些模型在通用计算机视觉方面表现出色，但它们在遥感（RS）领域的泛化潜力却尚未被充分探索。现有的方法通过生成视觉提示令牌来增强提示学习，但是依赖于完整图像特征，这会引入噪声和背景杂质，在同一类别内变化导致误分类问题。为了解决这一问题，我们提出了FrogDogNet，这是一种新颖的提示学习框架，它结合了傅里叶频率过滤和自注意力机制，以改善RS场景分类和领域泛化能力。FrogDogNet选择性地保留不变的低频成分，并消除噪声以及不相关的背景部分，在不同领域之间确保稳健的功能表示。该模型首先通过投影和自我注意提取重要特征，然后应用基于频率的过滤来保存提示学习所需的结构信息的关键细节。 在四个RS数据集和三项域泛化任务上的大量实验表明，FrogDogNet始终优于最先进的提示学习方法，并展示了跨领域转变的强大适应性。我们的研究结果强调了频基不变特性保留在推广中的有效性，为更广泛的应用铺平道路。我们的代码可在提供的链接获取。 （注意：原文中的"this https URL"应是一个具体的网页地址，这里未提供具体网址，因此只给出一般的说明信息。）
URL
https://arxiv.org/abs/2504.16433
