In recent years, the success of large-scale vision-language models (VLMs) such as CLIP has led to their increased usage in various computer vision tasks. These models enable zero-shot inference through carefully crafted instructional text prompts without task-specific supervision. However, the potential of VLMs for generalization tasks in remote sensing (RS) has not been fully realized. To address this research gap, we propose a novel image-conditioned prompt learning strategy called the Visual Attention Parameterized Prompts Learning Network (APPLeNet). APPLeNet emphasizes the importance of multi-scale feature learning in RS scene classification and disentangles visual style and content primitives for domain generalization tasks. To achieve this, APPLeNet combines visual content features obtained from different layers of the vision encoder and style properties obtained from feature statistics of domain-specific batches. An attention-driven injection module is further introduced to generate visual tokens from this information. We also introduce an anti-correlation regularizer to ensure discrimination among the token embeddings, as this visual information is combined with the textual tokens. To validate APPLeNet, we curated four available RS benchmarks and introduced experimental protocols and datasets for three domain generalization tasks. Our results consistently outperform the relevant literature and code is available at https://github.com/mainaksingha01/APPLeNet

翻译：近年来，大规模视觉语言模型（VLM）如CLIP的成功，推动了其在各类计算机视觉任务中的广泛应用。这类模型通过精心设计的指令性文本提示，无需任务特定监督即可实现零样本推理。然而，VLM在遥感图像泛化任务中的潜力尚未被充分发掘。为填补这一研究空白，我们提出了一种新颖的图像条件化提示学习策略——视觉注意力参数化提示学习网络（APPLeNet）。APPLeNet强调多尺度特征学习在遥感场景分类中的重要性，并分离了视觉风格与内容基元以应对域泛化任务。具体而言，APPLeNet融合了从视觉编码器不同层提取的视觉内容特征，以及从域特定批次特征统计中获得的风格属性，并进一步引入注意力驱动的注入模块，由此生成视觉标记。此外，我们提出一种反相关正则化项，以确保视觉信息与文本标记结合时，标记嵌入具有判别性。为验证APPLeNet的有效性，我们整理了四个公开遥感基准数据集，并针对三个域泛化任务设计了实验协议与数据集。实验结果持续优于相关文献，代码已开源至https://github.com/mainaksingha01/APPLeNet。