Lightweight Cross-View Localization via Quantized Knowledge Distillation and Adaptive Local Feature Matching

Despite notable progress in cross-view localization, extreme viewpoint differences between aerial and satellite imagery remain challenging. Existing methods often struggle to balance retrieval efficiency, matching accuracy, and model lightweightness. We propose a lightweight two-stage framework integrating efficient global retrieval with semantically-enhanced local matching. First, we adapt ternary quantized transformers to cross-view scenarios via progressive transfer learning, boosting retrieval without large-scale pretraining. Second, we introduce a dual-branch architecture that fuses texture and semantic features through cross-attention reasoning, employing semantic conditioning to filter mismatches from repetitive structures. Results on public datasets demonstrate improved localization accuracy and reduced computational cost, validating our framework's suitability for resource-constrained UAVs.