title: GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
summary: 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.
paper_authors._id: 682e1cd37a7eb705972c2cb7
paper_authors.name: Pengyue Jia
paper_authors._id: 682e1cd37a7eb705972c2cb8
paper_authors.name: Seongheon Park
paper_authors._id: 682e1cd37a7eb705972c2cb9
paper_authors.name: Song Gao
paper_authors._id: 682e1cd37a7eb705972c2cba
paper_authors.name: Xiangyu Zhao
paper_authors._id: 682e1cd37a7eb705972c2cbb
paper_authors.name: Yixuan Li
paper_title: GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
paper_summary: 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.
paper_ai_summary: GeoRanker uses large vision-language models to predict geographic proximity in image geolocalization by ranking multimodal candidate information.
paper_ai_keywords: vision-language models distance-aware ranking geographic proximity spatial relationships multi-order distance loss GeoRanking IM2GPS3K YFCC4K