STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification

• URL: http://arxiv.org/abs/2607.09057v1
• Date: Fri, 10 Jul 2026 02:56:34 GMT
• Title: STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification
• Abstract summary: Cross-view geo-localization (CVGL) aims to achieve GPS-free localization by matching drone-view images with corresponding satellite-view images.<n>Existing supervised methods rely on large-scale manually annotated cross-view image pairs, making them costly and difficult to scale.<n>We propose STEAM, an end-to-end unsupervised cross-view geo-localization framework that performs self-training directly on real drone and satellite images.
• Score: 7.7693155858168295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-view geo-localization (CVGL) aims to achieve GPS-free localization by matching drone-view images with corresponding satellite-view images. Existing supervised methods rely on large-scale manually annotated cross-view image pairs, making them costly and difficult to scale. In contrast, existing unsupervised approaches typically depend on generative models or clustering-based stage-wise optimization, which are prone to distribution bias and the accumulation of noisy pseudo-labels. To address these limitations, we propose STEAM (Stable Self-Training with Elastic Matching and Adaptive Purification), an end-to-end unsupervised cross-view geo-localization framework that performs self-training directly on real drone and satellite images. Specifically, the proposed Stable Spatial-Aware Module enhances the stability of feature representations, Elastic Matching discovers high-quality cross-view pseudo-labels, and Adaptive Purification dynamically maintains a reliable pseudo-label repository throughout the self-training process. Extensive experiments on the University-1652 and SUES-200 benchmarks demonstrate that STEAM achieves state-of-the-art performance among all existing unsupervised methods and delivers performance comparable to supervised approaches, validating the effectiveness and superiority of the proposed framework. The source code is available at https://github.com/wsx-heu/STEAM.git.

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