GeoMFD：基于几何感知适配器和边缘场蒸馏的持续无人机视角地理定位
GeoMFD: Continual Drone-View Geo-Localization with Geometry-Aware Adapter and Margin-Field Distillation
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中文总结 AI 辅助
研究持续无人机视角地理定位问题，提出GeoMFD方法，结合冷启动引导策略、几何感知适配器和边缘场蒸馏，平衡适应与跨视图几何保留，用单个持续更新模型实现与特定环境方法相当的性能，有效减轻遗忘。
中文摘要 AI 辅助
现有的无人机视角地理定位（DVGL）方法主要在静态训练范式下开发，模型针对固定环境提前利用所有训练数据进行优化。但该范式难以扩展到实际部署，因为无人机可能遇到各种环境，需要多个特定环境模型，会增加存储和模型选择成本。直接让单个模型适应新环境也可能扭曲先前学习的跨视图嵌入几何并导致遗忘。为应对这些挑战，我们形式化了持续无人机视角地理定位（C-DVGL）设置，并提出了GeoMFD，一种用于DVGL的几何感知持续适应方法。GeoMFD结合冷启动引导策略（CBS）、几何感知适配器（Geo-Adapter）和边缘场蒸馏（MFD）来平衡适应和跨视图几何保留。CBS初始化稳定嵌入空间，Geo-Adapter通过可控残差校正实现环境适应，MFD保留正样本对和硬负样本之间的相似性边缘以减轻跨视图几何遗忘。大量实验表明，GeoMFD有效减轻遗忘，并使用单个持续更新的模型实现了与特定环境DVGL方法相当的性能。
英文摘要
Existing drone-view geo-localization (DVGL) methods are mainly developed under a static training paradigm, where models are optimized for fixed environments with all training data available in advance. However, this paradigm is difficult to extend to real-world deployment, where drones may encounter diverse environments and require multiple environment-specific models, resulting in additional storage and model-selection costs. Directly adapting a single model to new environments also risks distorting previously learned cross-view embedding geometry and causing forgetting. To address these challenges, we formalize the continual drone-view geo-localization (C-DVGL) setting and propose GeoMFD, a geometry-aware continual adaptation method for DVGL. GeoMFD combines a cold-start bootstrapping strategy (CBS), a geometry-aware adapter (Geo-Adapter), and margin-field distillation (MFD) to balance adaptation and cross-view geometry preservation. CBS initializes a stable embedding space, Geo-Adapter enables environment adaptation through controlled residual corrections, and MFD preserves similarity margins between positive pairs and hard negatives to alleviate cross-view geometry forgetting. Extensive experiments demonstrate that GeoMFD effectively mitigates forgetting and achieves competitive performance with environment-specific DVGL methods using a single continuously updated model.
