  Cross-View Geo-Localization (CVGL) involves determining the geographical
location of a query image by matching it with a corresponding GPS-tagged
reference image. Current state-of-the-art methods predominantly rely on
training models with labeled paired images, incurring substantial annotation
costs and training burdens. In this study, we investigate the adaptation of
frozen models for CVGL without requiring ground truth pair labels. We observe
that training on unlabeled cross-view images presents significant challenges,
including the need to establish relationships within unlabeled data and
reconcile view discrepancies between uncertain queries and references. To
address these challenges, we propose a self-supervised learning framework to
train a learnable adapter for a frozen Foundation Model (FM). This adapter is
designed to map feature distributions from diverse views into a uniform space
using unlabeled data exclusively. To establish relationships within unlabeled
data, we introduce an Expectation-Maximization-based Pseudo-labeling module,
which iteratively estimates associations between cross-view features and
optimizes the adapter. To maintain the robustness of the FM's representation,
we incorporate an information consistency module with a reconstruction loss,
ensuring that adapted features retain strong discriminative ability across
views. Experimental results demonstrate that our proposed method achieves
significant improvements over vanilla FMs and competitive accuracy compared to
supervised methods, while necessitating fewer training parameters and relying
solely on unlabeled data. Evaluation of our adaptation for task-specific models
further highlights its broad applicability.
