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

Abstract

Cross-view geo-localization (CVGL) aims to perform visual localization by matching drone-view images with corresponding satellite images. Existing supervised methods rely on large-scale, manually annotated cross-view image pairs, which are costly and hard to scale. Many unsupervised approaches obtain pseudo-supervision through image-generation-based warm-up or clustering. These procedures may introduce distribution bias or propagate pseudo-label errors. To address these limitations, we propose STEAM (Stable Self-Training with Elastic Matching and Adaptive Purification), a direct self-training framework for unsupervised cross-view geo-localization. It combines end-to-end representation learning with iterative pseudo-label refinement without image-generation-based warm-up or clustering. Specifically, the Stable Spatial-Aware Module constructs image descriptors through bounded nonlinear spatial weighting. Elastic Matching combines Bidirectional Top-K Soft Matching with Dynamic Threshold Filtering to accommodate many-to-one cross-view correspondences while filtering ambiguous candidates. Adaptive Purification maintains the pseudo-label repository through Confidence-Aware Updates, Age-Aware Updates, and Expired Label Removal. These operations respectively update assignments through confidence comparisons, track consecutive epochs without agreement or successful replacement, and discard entries whose age reaches a preset limit. On University-1652, STEAM achieves the highest R@1 and AP among the compared unsupervised methods in both retrieval directions. It also achieves competitive performance on SUES-200 and DenseUAV.