STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification
Read the original on arXiv Computer Vision →STEAM is a self‑training framework for unsupervised cross‑view geo‑localization that eliminates the need for image‑generation warm‑ups or clustering. It introduces a Stable Spatial‑Aware Module for bounded nonlinear spatial weighting, Elastic Matching with Bidirectional Top‑K Soft Matching and Dynamic Threshold Filtering, and Adaptive Purification that updates pseudo‑labels based on confidence, age, and expiration. On the University‑1652 dataset, STEAM outperforms other unsupervised methods in both retrieval directions and shows competitive results on SUES‑200 and DenseUAV.
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