Visual cross view geo-localization is generally approached within a joint retrieval-and-calibration framework. However, existing methods overlook semantic ambiguities arising from query and reference images characterized by low overlap, dynamic foregrounds, viewpoint changes, and perceptual aliasing. This makes it challenging to automatically control the relative importance of the two tasks, potentially compromising the retrieval task in favor of the offset regression. Consequently, the model may encounter conflicting dominating gradients during joint training. To address this, we propose to model the semantic ambiguity during the offset regression process by integrating associated uncertainty scores, represented as 2D Gaussian distributions, to mitigate negative transfer effects within the joint tasks. We further introduce an uncertainty-aware similarity metric to enhance similarity assessment between query and reference images, accounting for their semantic ambiguities. This metric propagates uncertainty scores into the retrieval task, focusing on certain samples and learning discriminative feature embeddings, allowing the model to adaptively handle conflicting dominating gradients during joint training. Extensive experiments demonstrate that our method improves the overall performance of the joint tasks, achieving state-of-the-art results on the VIGOR and CVACT datasets.

Fine-grained cross-view localization seeks to predict ground-level camera positions within GPS-tagged aerial images by matching ground and aerial views. Existing methods often rely on large-scale ground truth annotations from specific regions, but performance degrades due to domain shifts when models trained in one area are applied to another. However, collecting region-specific annotations for each area is costly or infeasible. To address this, we propose a self-distillation curriculum learning framework that generalizes pretrained localization models to unseen new areas. Our approach introduces a Dirichlet-based quality assessment strategy to evaluate teacher-generated pseudo labels, where high uncertainty signals noisy predictions and low uncertainty indicates clean samples. This uncertainty is used to guide an easy-to-hard curriculum learning strategy, where easy samples are prioritized initially, and more challenging samples are progressively incorporated, enabling effective student training. Furthermore, we develop a joint optimization scheme that updates both the student model and pseudo labels, applying adaptive label smoothing to mitigate label noises and taking full advantage of new area data. Extensive experimental results on the VIGOR and KITTI benchmarks demonstrate that our method outperforms state-of-the-art approaches in new area localization, achieving superior accuracy without additional supervision.