NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization

Jordan Boyd-Graber; Runze Li; Tasnim Kabir; Zheyuan Zhang

NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization

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arxiv 2502.14638 v1 pith:HWULDXXE submitted 2025-02-20 cs.CL cs.CV

NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization

Zheyuan Zhang , Runze Li , Tasnim Kabir , Jordan Boyd-Graber This is my paper

classification cs.CL cs.CV

keywords imagelanguagemodelsnavigreasoningdatasetgeo-localizationhigh-quality

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Image geo-localization is the task of predicting the specific location of an image and requires complex reasoning across visual, geographical, and cultural contexts. While prior Vision Language Models (VLMs) have the best accuracy at this task, there is a dearth of high-quality datasets and models for analytical reasoning. We first create NaviClues, a high-quality dataset derived from GeoGuessr, a popular geography game, to supply examples of expert reasoning from language. Using this dataset, we present Navig, a comprehensive image geo-localization framework integrating global and fine-grained image information. By reasoning with language, Navig reduces the average distance error by 14% compared to previous state-of-the-art models while requiring fewer than 1000 training samples. Our dataset and code are available at https://github.com/SparrowZheyuan18/Navig/.

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