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

TL;DR

This paper introduces a new hierarchical retrieval framework called Dynamic Contrastive Learning (DyCL) for cross-view geo-localization, leveraging a novel benchmark and demonstrating significant accuracy improvements over existing methods.

Contribution

The paper presents DyCL, a novel contrastive learning framework that aligns features hierarchically for geo-localization, supported by a new benchmark dataset DA-Campus.

Findings

DyCL significantly improves hierarchical retrieval accuracy.

DyCL complements existing metric learning methods effectively.

The DA-Campus benchmark enables systematic evaluation of distance-aware geo-localization.

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.