SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization

TL;DR

SinGeo introduces a robust single-model framework for cross-view geo-localization that outperforms existing methods across diverse conditions without needing multiple models or explicit transformations.

Contribution

The paper presents SinGeo, a novel framework with dual discriminative learning and curriculum strategies, achieving state-of-the-art results and robustness in cross-view geo-localization.

Findings

Sets new SOTA results on four benchmark datasets.

Outperforms methods trained for extreme FoVs.

Demonstrates cross-architecture transferability.

Abstract

Robust cross-view geo-localization (CVGL) remains challenging despite the surge in recent progress. Existing methods still rely on field-of-view (FoV)-specific training paradigms, where models are optimized under a fixed FoV but collapse when tested on unseen FoVs and unknown orientations. This limitation necessitates deploying multiple models to cover diverse variations. Although studies have explored dynamic FoV training by simply randomizing FoVs, they failed to achieve robustness across diverse conditions -- implicitly assuming all FoVs are equally difficult. To address this gap, we present SinGeo, a simple yet powerful framework that enables a single model to realize robust cross-view geo-localization without additional modules or explicit transformations. SinGeo employs a dual discriminative learning architecture that enhances intra-view discriminability within both ground and satellite branches, and is the first to introduce a curriculum learning strategy to achieve robust CVGL. Extensive evaluations on four benchmark datasets reveal that SinGeo sets state-of-the-art (SOTA) results under diverse conditions, and notably outperforms methods specifically trained for extreme FoVs. Beyond superior performance, SinGeo also exhibits cross-architecture transferability. Furthermore, we propose a consistency evaluation method to quantitatively assess model stability under varying views, providing an explainable perspective for understanding and advancing robustness in future CVGL research. Codes will be available upon acceptance.