CRAD: Confused Relation-Aware Distillation for Efficient Cross-View Geo-Localization

Cross-view geo-localization aims to match ground-level images with satellite-view counterparts and is a core capability for autonomous navigation. However, state-of-the-art models are often computationally expensive, motivating the use of knowledge distillation for efficient deployment. Similarity-distribution distillation has been explored in deep metric learning, but compact cross-view geo-localization requires transferring relations between satellite queries and ground candidates while learning from informative negatives. We propose Confused Relation-Aware Distillation (CRAD), which distills a pretrained, frozen teacher into a compact student. CRAD aligns cross-view candidate distributions and jointly trains on student-student and teacher-student satellite-ground correspondences. To further strengthen supervision, we adopt Teacher-Guided confused Sampling, which constructs a shared hard-sample pool by jointly considering samples that are challenging for both the teacher and the student, allowing the teacher to actively select confused examples to guide the student’s training. Extensive experiments on multiple benchmarks demonstrate the effectiveness of CRAD. On the challenging VIGOR-Same benchmark, CRAD-Tiny (28.6M) achieves 75.16% Recall@1, while the lightweight CRAD-Nano (15.0M) still delivers competitive performance. These results indicate that, for metric learning–based geo-localization, distilling how to compare can be more effective than distilling what to represent.