Query-Driven Feature Learning for Cross-View Geo-Localization

The cross-view geo-localization task aims to accurately retrieve a location using images captured from different platforms, such as satellites and drones, which is particularly challenging due to a large variation in viewpoint. Current methods mainly focus on rigid strategies like partitioning or sorting local features, which may be ill-suited to accommodate the variance of viewpoint and distance scale in different camera perspectives. To address these issues, we propose a novel method called query-driven feature learning (QDFL) to query viewpoint-invariant feature vectors autonomously. Our method incorporates an adaptive query embedding unit (AQEU) and a feature fusion unit (FFU). AQEU adjusts feature map and implements a coarse query process to extract the contextual clues. FFU further refines feature map, fusing it at spatial and channel dimensions, tending to withdraw more fine-grained features. Subsequently, AQEU executes a fine query on salient landmarks in the fused feature map, enhancing the minutia descriptive power of query vectors. Additionally, we employ parameter-efficient transfer learning manner by integrating tunable adapters into the frozen pre-trained backbone, maintaining feature representation capabilities of foundation models while enabling seamless adaptation to cross-view geo-localization task. Extensive experiments show that our method achieves state-of-the-art performances on two well-known datasets, University-1652 and SUES-200. Moreover, our method exhibits an excellent generalizability compared with current state-of-the-art methods in cross-dataset experiments. The code is available at https://github.com/Shuyu-Hu/QDFL.