Cross-Distill: Multi-Manifold and Viewpoint-Decoupled Distillation for Cross-View Geo-Localization

Abstract

Abstract— Cross-View Geo-Localization (CVGL) localizes a query image via retrieval from georeferenced satellite imagery,yet severe viewpoint variation remains a central challenge. Recent advances often rely on heavy backbones or add-on modules that achieve high accuracy but are impractical on resource-constrained UAVs. To balance accuracy and efficiency, we introduce Cross-Distill, a knowledge-distillation framework for CVGL. Cross-Distill performs Cross-Similarity Ranking Distillation by constructing a teacher-student interaction matrix to enforce ranking consistency and enhance discrimination. Building on this, it introduces Viewpoint Decoupling, which partitions ranking relations into intra-view, intra-to-cross-view, and cross-to-cross-view, enabling precise modeling of cross-view dependencies and improving class compactness and separability. Cross-Distill further employs Multi-Manifold Feature Distillation that jointly enforces angular consistency on the spherical manifold, preserves local distances in Euclidean space, and leverages hyperbolic distance as a negatively curved metric to strengthen teacher–student alignment. Experiments on University-1652 and SUES-200 show that the distilled student achieves significant gains with low complexity (31.43M parameters, 13.09 GFLOPs),and an inference time of only 62.02 ms per image on an RK3588. For instance, on University-1652 UAV→SAT retrieval, R@1 improves from 75.97% to 94.43% and AP from 79.24% to 95.33%.