针对无人机与卫星图像定位难题，提出双层渐进式难例重加权方法。
Dual-level Progressive Hardness-Aware Reweighting for Cross-View Geo-Localization
- 分样本与批次两级设计，动态评估难例并分配权重。
- 在University-1652和SUES-200上均超越现有最优方法。
- 适合解决视角差异大、硬负样本多的跨视图定位任务。
无人机与卫星图像间的跨视图地理定位（CVGL）因视角差异大及存在视觉相似但地理不匹配的硬负样本而极具挑战。现有挖掘或重加权策略多采用静态权重，对分布变化敏感，易过早强调难例，导致梯度噪声和训练不稳定。本文提出双层渐进式难例感知重加权（DPHR）策略：在样本层面，基于比率的难度感知（RDA）模块评估相对难度并为负样本分配细粒度权重；在批次层面，渐进自适应损失加权（PALW）机制利用训练进度信号，在早期抑制噪声梯度，随训练成熟逐步增强硬负样本挖掘。在University-1652与SUES-200基准上的实验表明，DPHR在性能与鲁棒性上均显著优于当前最优方法。
原文摘要 · Abstract (English)
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.
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