Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization

Existing deep learning-based cross-view geolocalization methods primarily focus on improving the accuracy of cross-domain image matching. Less attention is paid to ensuring that models can comprehensively capture contextual information around the target and minimize the cost of localization errors. To support quantitative research into this Distance-Aware Cross-View Geo-Localization (DACVGL) problem, we construct Distance-Aware Campus (DA-Campus), the first benchmark that pairs multi-view imager