SkyLink：一种由大型视觉-语言模型驱动的跨视图无人机地理定位重排序框架
SkyLink: A Large Vision-Language Model Driven Re-ranking Framework for Cross-View UAV geolocalization
AI总结：
本文提出SkyLink框架，利用大型视觉-语言模型建模无人机与卫星视图间的视觉语义关系，通过引入关系感知损失提升跨视图地理定位的排序效果。
摘要：
跨视图无人机地理定位本质上是一个大规模图像检索任务，旨在通过匹配查询无人机与大规模地理标记的卫星图像数据库来确定无人机的地理坐标。现有方法为每个视图学习独立的特征表示，并通过简单的启发式方法评估特征相似性，从而忽略了关键的跨视图关系。本文提出SkyLink，一种新颖的即插即用排序框架，首次引入了跨视图关系的联合建模以提升跨视图无人机地理定位。SkyLink利用大型视觉-语言模型（LVLM）建模无人机与卫星视图之间的复杂视觉-语义关系，促进有效的跨视图匹配。为进一步优化学习过程，我们引入了关系感知损失。它利用软标签提供更精细的监督信号，缓解对近正样本的严厉惩罚。这种方法提高了训练稳定性和模型的判别能力。在多个基础检索架构和基准数据集上进行的大量实验表明，SkyLink显著提升了现有模型的排序效果，在各种具有挑战性的场景中均实现了优越的性能。
英文摘要：
Cross-view UAV geolocalization is fundamentally a challenging large-scale image retrieval task, aiming to determine the geographic coordinates of Unmanned Aerial Vehicle (UAV) queries by matching them against an extensive geo-tagged satellite image database. Most existing methods learn separate feature representations for each view and determine the final prediction using naive heuristics to assess feature similarity, thereby neglecting to model the crucial cross-view relationships. In this paper, we propose SkyLink, a novel plug-and-play ranking framework that pioneers joint relational modeling of inter-view relationships to enhance cross-view UAV geolocalization. SkyLink leverages a Large Vision-Language Model (LVLM) to model the intricate visual-semantic relationships between UAV and satellite views, facilitating effective cross-view matching. To further refine the learning process, we introduce a relational-aware loss. It leverages soft labels to provide a more nuanced supervision signal, mitigating the harsh penalty on near-positive pairs. This approach enhances both training stability and the model's discriminative capacity. Extensive experiments conducted across multiple base retrieval architectures and benchmark datasets demonstrate that SkyLink significantly boosts the ranking effectiveness of existing models, consistently achieving superior performance in various challenging scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
