GeoRouter: Dynamic Paradigm Routing for Worldwide Image Geolocalization
Pengyue Jia, Derong Xu, Yingyi Zhang, Xiaopeng Li, Wenlin Zhang, Yi Wen, Yuanshao Zhu, Xiangyu Zhao

TL;DR
GeoRouter is a novel adaptive framework that dynamically routes image geolocalization queries between retrieval and generation paradigms, leveraging a large-scale dataset and a distance-aware training objective to improve accuracy.
Contribution
It introduces a dynamic routing approach for geolocalization that combines retrieval and generation paradigms, along with a new dataset and a distance-aware training method.
Findings
GeoRouter outperforms existing methods on IM2GPS3k and YFCC4k datasets.
The distance-aware preference objective improves paradigm selection accuracy.
Adaptive routing enhances geolocalization precision across diverse images.
Abstract
Worldwide image geolocalization aims to predict precise GPS coordinates for images captured anywhere on Earth, which is challenging due to the large visual and geographic diversity. Recent methods mainly follow two paradigms: retrieval-based approaches that match queries against a reference database, and generation-based approaches that directly predict coordinates using Large Vision-Language Models (LVLMs). However, we observe distinct error profiles between them: retrieval excels at fine-grained instance matching, while generation offers robust semantic reasoning. This complementary heterogeneity suggests that no single paradigm is universally superior. To harness this potential, we propose GeoRouter, a dynamic routing framework that adaptively assigns each query to the optimal paradigm. GeoRouter leverages an LVLM backbone to analyze visual content and provide routing decisions. To…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Robotics and Sensor-Based Localization
