[2603.27251] Zero-shot Vision-Language Reranking for Cross-View Geolocalization

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.27251 (cs) [Submitted on 28 Mar 2026] Title:Zero-shot Vision-Language Reranking for Cross-View Geolocalization Authors:Yunus Talha Erzurumlu, John E. Anderson, William J. Shuart, Charles Toth, Alper Yilmaz View a PDF of the paper titled Zero-shot Vision-Language Reranking for Cross-View Geolocalization, by Yunus Talha Erzurumlu and 4 other authors View PDF HTML (experimental) Abstract:Cross-view geolocalization (CVGL) systems, while effective at retrieving a list of relevant candidates (high Recall@k), often fail to identify the single best match (low Top-1 accuracy). This work investigates the use of zero-shot Vision-Language Models (VLMs) as rerankers to address this gap. We propose a two-stage framework: state-of-the-art (SOTA) retrieval followed by VLM reranking. We systematically compare two strategies: (1) Pointwise (scoring candidates individually) and (2) Pairwise (comparing candidates relatively). Experiments on the VIGOR dataset show a clear divergence: all pointwise methods cause a catastrophic drop in performance or no change at all. In contrast, a pairwise comparison strategy using LLaVA improves Top-1 accuracy over the strong retrieval baseline. Our analysis concludes that, these VLMs are poorly calibrated for absolute relevance scoring but are effective at fine-grained relative visual judgment, making pairwise reranking a promising direction for enhancing CVGL precision. Comments: Subjects...