HALO: Hierarchical Adaptive Feature Learning and Cross-View Interaction for Object-Level Geo-Localization

Cross-view object localization (CVOGL) offers fine-grained geographic information regarding the specified object of interest, in comparison to typical cross-view geo-location at the image level. However, it faces more challenges, primarily due to significant visual appearance changes induced by viewpoint discrepancies and the difficulty of accurately identifying corresponding objects within reference images containing multiple targets. To address these challenges, we propose a novel CVOGL model termed HALO. First, we introduce a cascade cross-view feature interaction module that enables effective local-to-global feature fusion across different viewpoints, thereby enhancing the feature representation of objects in the reference image. Second, to mitigate the scale variations and feature distribution discrepancies between the query and reference images, we propose an adaptive feature hierarchization and aggregate module. It extracts more representative global descriptors by adaptively hierarchizing and aggregating features. Lastly, utilizing the learned global descriptors, we perform cross-view CL and introduce a hard sample mining strategy to further enhance the discriminative ability of the network. Through these advancements, we significantly improve the discriminative representations of the query objects at both views. Extensive experiments on the CVOGL dataset demonstrate the effectiveness and robustness of the proposed method. In the CVOGL task of drone→satellite, HALO improves 2.36% in [email protected] on the test sets. Similarly, HALO shows an increase of 2.49% in [email protected] on the validation set for the CVOGL task of ground→satellite. Our codes are publicly available at https://github.com/ZehaoZhang-Uestc/HALO.