NAVIG: Natural Language-guided Analysis with Vision Language Models for
  Image Geo-localization
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/.
67b8760bf8311235c642d7a4
Zheyuan Zhang
claimed_verified
2025-02-21T14:42:43.700Z
648c4af819bb04c06467189c
/avatars/8b9372a233d4c00b555625fa7b5203e2.svg
Zheyuan Zhang
user
Zheyuan22
67b8760bf8311235c642d7a5
Runze Li
claimed_verified
2025-02-24T09:21:43.993Z
66482dea53317bdff335b006
/avatars/5079ed975c5689739e1e8e4ae8a47a3d.svg
RUNZE LI
user
huggingCode11
67b8760bf8311235c642d7a6
Tasnim Kabir
admin_assigned
2025-02-21T15:01:51.663Z
6676067b8a4064c02b4ef6b7
/avatars/319ff99740183793cab3045ae3bf1395.svg
Tasnim Kabir
user
TasnimKabir12
67b8760bf8311235c642d7a7
Jordan Boyd-Graber
NAVIG: Natural Language-guided Analysis with Vision Language Models for
  Image Geo-localization
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/.
A new image geo-localization framework, Navig, integrates global and fine-grained image information with language to reduce average distance error by 14% using a high-quality dataset derived from GeoGuessr.
Vision Language Models image geo-localization dataset geographical contexts cultural contexts global and fine-grained image information language reasoning