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

2025-06-28

AI Summary

To address the dual challenges of insufficient contextual modeling and high localization error cost in cross-view geo-localization, this paper introduces Distance-Aware Cross-View Geo-Localization (DACVGL)—a novel task—and presents DA-Campus, the first multi-resolution campus dataset with precise ground-truth distance annotations. Methodologically, we propose Dynamic Contrastive Learning (DyCL), a framework that enforces hierarchical spatial margin constraints to achieve progressive cross-domain feature alignment. DyCL replaces conventional metric learning with a joint hierarchical retrieval and multi-scale feature alignment mechanism to better capture spatial relationships. Experiments demonstrate that DyCL significantly improves both hierarchical retrieval performance and absolute localization accuracy on DA-Campus, outperforming state-of-the-art methods. The code and dataset are publicly released.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Existing deep learning-based cross-view geo-localization methods primarily focus on improving the accuracy of cross-domain image matching, rather than enabling models to comprehensively capture contextual information around the target and minimize the cost of localization errors. To support systematic 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 imagery with precise distance annotations across three spatial resolutions. Based on DA-Campus, we formulate DACVGL as a hierarchical retrieval problem across different domains. Our study further reveals that, due to the inherent complexity of spatial relationships among buildings, this problem can only be addressed via a contrastive learning paradigm, rather than conventional metric learning. To tackle this challenge, we propose Dynamic Contrastive Learning (DyCL), a novel framework that progressively aligns feature representations according to hierarchical spatial margins. Extensive experiments demonstrate that DyCL is highly complementary to existing multi-scale metric learning methods and yields substantial improvements in both hierarchical retrieval performance and overall cross-view geo-localization accuracy. Our code and benchmark are publicly available at https://github.com/anocodetest1/DyCL.

Problem

• Improving contextual understanding in cross-view geo-localization
• Addressing hierarchical retrieval across different spatial domains
• Enhancing accuracy with dynamic contrastive learning for spatial margins

Innovation

• Dynamic Contrastive Learning for hierarchical alignment
• Distance-Aware Campus benchmark with precise annotations
• Hierarchical retrieval via contrastive spatial margins