Dual-level Progressive Hardness-Aware Reweighting for Cross-View Geo-Localization
This work addresses the problem of accurate geo-localization for drone and satellite imagery, which is incremental as it builds on existing reweighting strategies with a novel progressive approach.
The paper tackles the challenge of cross-view geo-localization between drone and satellite imagery by addressing severe viewpoint gaps and hard negatives, proposing a dual-level progressive hardness-aware reweighting strategy that achieves consistent improvements over state-of-the-art methods on benchmarks like University-1652 and SUES-200.
Cross-view geo-localization (CVGL) between drone and satellite imagery remains challenging due to severe viewpoint gaps and the presence of hard negatives, which are visually similar but geographically mismatched samples. Existing mining or reweighting strategies often use static weighting, which is sensitive to distribution shifts and prone to overemphasizing difficult samples too early, leading to noisy gradients and unstable convergence. In this paper, we present a Dual-level Progressive Hardness-aware Reweighting (DPHR) strategy. At the sample level, a Ratio-based Difficulty-Aware (RDA) module evaluates relative difficulty and assigns fine-grained weights to negatives. At the batch level, a Progressive Adaptive Loss Weighting (PALW) mechanism exploits a training-progress signal to attenuate noisy gradients during early optimization and progressively enhance hard-negative mining as training matures. Experiments on the University-1652 and SUES-200 benchmarks demonstrate the effectiveness and robustness of the proposed DPHR, achieving consistent improvements over state-of-the-art methods.