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

ACL ARR 2024 December Submission1856 Authors

Abstract: 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://anonymous.4open.science/r/Navig-8788.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: multimodal applications, image geo-localization

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 1856