GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Worldwide image geolocalization-the task of predicting GPS coordinates from
images taken anywhere on Earth-poses a fundamental challenge due to the vast
diversity in visual content across regions. While recent approaches adopt a
two-stage pipeline of retrieving candidates and selecting the best match, they
typically rely on simplistic similarity heuristics and point-wise supervision,
failing to model spatial relationships among candidates. In this paper, we
propose GeoRanker, a distance-aware ranking framework that leverages large
vision-language models to jointly encode query-candidate interactions and
predict geographic proximity. In addition, we introduce a multi-order distance
loss that ranks both absolute and relative distances, enabling the model to
reason over structured spatial relationships. To support this, we curate
GeoRanking, the first dataset explicitly designed for geographic ranking tasks
with multimodal candidate information. GeoRanker achieves state-of-the-art
results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly
outperforming current best methods.
682e1cd37a7eb705972c2cb7
Pengyue Jia
682e1cd37a7eb705972c2cb8
Seongheon Park
682e1cd37a7eb705972c2cb9
Song Gao
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Xiangyu Zhao
682e1cd37a7eb705972c2cbb
Yixuan Li
GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Worldwide image geolocalization-the task of predicting GPS coordinates from
images taken anywhere on Earth-poses a fundamental challenge due to the vast
diversity in visual content across regions. While recent approaches adopt a
two-stage pipeline of retrieving candidates and selecting the best match, they
typically rely on simplistic similarity heuristics and point-wise supervision,
failing to model spatial relationships among candidates. In this paper, we
propose GeoRanker, a distance-aware ranking framework that leverages large
vision-language models to jointly encode query-candidate interactions and
predict geographic proximity. In addition, we introduce a multi-order distance
loss that ranks both absolute and relative distances, enabling the model to
reason over structured spatial relationships. To support this, we curate
GeoRanking, the first dataset explicitly designed for geographic ranking tasks
with multimodal candidate information. GeoRanker achieves state-of-the-art
results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly
outperforming current best methods.
GeoRanker uses large vision-language models to predict geographic proximity in image geolocalization by ranking multimodal candidate information.
vision-language models distance-aware ranking geographic proximity spatial relationships multi-order distance loss GeoRanking IM2GPS3K YFCC4K