MUTUAL RELATIVE POSITION LEARNING TRANSFORMER FOR CROSS-VIEW GEO-LOCALIZATION

Cross-view geo-localization refers to matching ground images with geo-tagged satellite imagery. Existing methods are mainly two-stage, applying a polar transform to roughly eliminate the gap between these two domains, but this might introduce distortions and reduce the discriminativeness of features. In this work, we propose a transformer-based one-stage approach, which unifies gap elimination and feature extraction. The relative position among objects provides critical clues for this task and has strong spatial correspondences between the two views. Firstly, we form the relative position by selecting representative tokens from different regions. Then the relative positions of the two views predict each other and eliminate the gap through mutual learning. Finally, we introduce a novel consistency loss to enhance feature learning by mutual transfer of relational knowledge among samples. Extensive experiments demonstrate that our method achieves state-of-the-art results on both standard and fine-grained datasets.