CLNet: Cross-View Correspondence Makes a Stronger Geo-Localizationer

TL;DR

CLNet introduces a novel framework for cross-view geo-localization that explicitly models spatial correspondences, significantly improving accuracy by combining semantic and geometric feature alignment.

Contribution

The paper presents CLNet, a new correspondence-aware framework with modules for explicit spatial alignment, feature remapping, and channel reweighting, advancing cross-view geo-localization methods.

Findings

Achieves state-of-the-art results on four benchmarks.

Effectively models explicit spatial correspondences.

Improves interpretability and generalizability.

Abstract

Image retrieval-based cross-view geo-localization (IRCVGL) aims to match images captured from significantly different viewpoints, such as satellite and street-level images. Existing methods predominantly rely on learning robust global representations or implicit feature alignment, which often fail to model explicit spatial correspondences crucial for accurate localization. In this work, we propose a novel correspondence-aware feature refinement framework, termed CLNet, that explicitly bridges the semantic and geometric gaps between different views. CLNet decomposes the view alignment process into three learnable and complementary modules: a Neural Correspondence Map (NCM) that spatially aligns cross-view features via latent correspondence fields; a Nonlinear Embedding Converter (NEC) that remaps features across perspectives using an MLP-based transformation; and a Global Feature Recalibration (GFR) module that reweights informative feature channels guided by learned spatial cues. The proposed CLNet can jointly capture both high-level semantics and fine-grained alignments. Extensive experiments on four public benchmarks, CVUSA, CVACT, VIGOR, and University-1652, demonstrate that our proposed CLNet achieves state-of-the-art performance while offering better interpretability and generalizability.