BEMN: Balanced Bias Enhanced Multi-Branch Network for Cross-View Geo-Localization

Cross-view geo-localization (CVGL) offers a promising alternative for positioning in GNSS-constrained environments through visual matching techniques. Extreme viewpoint variations and the complexity of real-world scenes present significant challenges to this task. However, current methods primarily focus on learning single-scale features, which may be inadequate for practical applications. Although some approaches attempt to incorporate multi-scale representations, they may suffer from unimodal bias arising from structural discrepancies among model branches, limiting effective multi-scale feature extraction. To address these issues, we propose a fully multi-branch network architecture, named BEMN, which is designed to learn multi-scale robust feature representations. Specifically, we construct a multi-branch backbone network based on pretrained visual models and design a two-stage training strategy. In the first stage, a separate training scheme is employed to thoroughly optimize each branch of the network, and a joint feature alignment (JFA) module is introduced to align cross-view features. The entire network is fine-tuned in the second stage, where a frequency domain adjustment (FDA) module is designed to improve performance. To further assess the generalization ability of CVGL methods, we establish Xian-37, a highly challenging CVGL test dataset featuring complex real scenes captured from diverse platforms and viewpoints. Experimental results across multiple public benchmarks validate the superiority of our approach, achieving state-of-the-art performance and demonstrating outstanding generalization capabilities. Our code and model are available at https://github.com/VERYBC/BEMN.