Recurrent Cross-View Object Geo-Localization

Cross-view object geo-localization (CVOGL) aims to deter-mine the location of a specific object in high-resolution satellite imagerygiven a query image with a point prompt. Existing approaches treatCVOGL as a one-shot detection process, directly regressing object loca-tions from cross-view information aggregation, but they are vulnerableto feature noise and lack mechanisms for error correction. In this paper,we propose ReCOT, a Recurrent Cross-view Object geo-localizationTransformer, which models CVOGL as a recurrent localization process.ReCOT introduces a set of learnable tokens that encode task-specificintent from the query image and prompt embeddings, and iteratively at-tend to the reference features to refine the predicted location. To enhancethis recurrent process, we incorporate two complementary modules: (1) aSAM-based knowledge distillation strategy that transfers segmentationpriors from the Segment Anything Model (SAM) to provide clearer se-mantic guidance without additional inference cost, and (2) a ReferenceFeature Enhancement Module (RFEM) that introduces hierarchical at-tention to emphasize object-relevant regions in the reference features.Extensive experiments on CVOGL benchmarks demonstrate that Re-COT achieves state-of-the-art (SOTA) performance while significantlyreducing parameters compared to previous SOTA approaches. Our codeis available at https://github.com/Temperature-ai/ReCOT.git.