NAVIG: Natural Language-guided Analysis with Vision Language Models for
  Image Geo-localization
Authors: name: Zheyuan Zhang, name: Runze Li, name: Tasnim Kabir, name: Jordan Boyd-Graber

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/.