无需标注数据，通过对抗桥接与图校准实现跨视角地理定位突破
UniABG: Unified Adversarial View Bridging and Graph Correspondence for Unsupervised Cross-View Geo-Localization
- 先用对抗方法对齐不同视角特征，再构建双图结构校准匹配关系
- 在University-1652和SUES-200上分别提升10.63%和16.73%的定位准确率
- 适合无标注数据场景下的高精度地理定位任务
跨视角地理定位（CVGL）旨在将查询图像（如无人机图像）与对应地理位置的反向视角图像（如卫星图）进行匹配。尽管监督方法表现优异，但依赖大量成对标注限制了其可扩展性。无监督方法虽避免标注成本，却因视角间固有域差异导致伪标签噪声严重。为此，本文提出UniABG，一种新颖的双阶段无监督跨视角地理定位框架，融合对抗视角桥接与基于图的对应关系校准。首先采用视图感知对抗桥接（VAAB）建模视角不变特征，提升伪标签鲁棒性；随后通过异构图滤波校准（HGFC）构建双视角结构图，实现可靠视图对应。大量实验表明，UniABG达到当前最优无监督性能，在University-1652上卫星→无人机平均精度提升+10.63%，在SUES-200上提升+16.73%，甚至超越部分监督基线。源代码见https://github.com/chenqi142/UniABG。
原文摘要 · Abstract (English)
Cross-view geo-localization (CVGL) matches query images ($\textit{e.g.}$, drone) to geographically corresponding opposite-view imagery ($\textit{e.g.}$, satellite). While supervised methods achieve strong performance, their reliance on extensive pairwise annotations limits scalability. Unsupervised alternatives avoid annotation costs but suffer from noisy pseudo-labels due to intrinsic cross-view domain gaps. To address these limitations, we propose $\textit{UniABG}$, a novel dual-stage unsupervised cross-view geo-localization framework integrating adversarial view bridging with graph-based correspondence calibration. Our approach first employs View-Aware Adversarial Bridging (VAAB) to model view-invariant features and enhance pseudo-label robustness. Subsequently, Heterogeneous Graph Filtering Calibration (HGFC) refines cross-view associations by constructing dual inter-view structure graphs, achieving reliable view correspondence. Extensive experiments demonstrate state-of-the-art unsupervised performance, showing that UniABG improves Satellite $\rightarrow$ Drone AP by +10.63\% on University-1652 and +16.73\% on SUES-200, even surpassing supervised baselines. The source code is available at https://github.com/chenqi142/UniABG
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