Multi-Level Embedding and Alignment Network with Consistency and Invariance Learning for Cross-View Geo-Localization

TL;DR

This paper introduces MEAN, a lightweight multi-level embedding and alignment network that improves cross-view geo-localization by learning robust, invariant features with reduced computational costs, outperforming existing models.

Contribution

The paper presents a novel lightweight network with multi-level enhancement and cross-domain alignment for effective cross-view feature matching in geo-localization.

Findings

Reduces model parameters by 62.17%.

Decreases computational complexity by 70.99%.

Achieves competitive or superior localization accuracy.

Abstract

Cross-View Geo-Localization (CVGL) involves determining the localization of drone images by retrieving the most similar GPS-tagged satellite images. However, the imaging gaps between platforms are often significant and the variations in viewpoints are substantial, which limits the ability of existing methods to effectively associate cross-view features and extract consistent and invariant characteristics. Moreover, existing methods often overlook the problem of increased computational and storage requirements when improving model performance. To handle these limitations, we propose a lightweight enhanced alignment network, called the Multi-Level Embedding and Alignment Network (MEAN). The MEAN network uses a progressive multi-level enhancement strategy, global-to-local associations, and cross-domain alignment, enabling feature communication across levels. This allows MEAN to effectively connect features at different levels and learn robust cross-view consistent mappings and modality-invariant features. Moreover, MEAN adopts a shallow backbone network combined with a lightweight branch design, effectively reducing parameter count and computational complexity. Experimental results on the University-1652 and SUES-200 datasets demonstrate that MEAN reduces parameter count by 62.17% and computational complexity by 70.99% compared to state-of-the-art models, while maintaining competitive or even superior performance. Our code and models will be released on https://github.com/ISChenawei/MEAN.