2026-06-04原文2 min readUpdated: 2026-06-04

Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

Pinpoint is a new image geolocation method that combines internet photos and street-view imagery using a retrieve-and-rerank architecture. It trains a contrastive embedder on both Flickr and street-view data to learn a shared image-GPS space, then uses an attention-based reranker to rescore candidates. Without relying on multimodal LLMs, it achieves state-of-the-art results on multiple benchmarks.

SourcearXiv Computer VisionAuthor: Nika Chuzhoy, Brian Hu, Amit A. Arora, Jae Ro, Sarthak S. Sahu

[2606.04133] Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

[Submitted on 2 Jun 2026]

Title:Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

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Abstract:Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evidence is often ambiguous, diverse, and unevenly distributed. Prior work has typically treated geolocation of ordinary internet photos and street-view imagery as separate tasks, despite their complementary strengths: internet photos better match the appearance distribution of user-captured queries, while street-view imagery provides denser, geographically grounded coverage. We present Pinpoint, a retrieve-and-rerank architecture that combines both sources in a coarse-to-fine pipeline. A contrastive image-GPS embedder is trained on both user-uploaded Flickr photos and street-view imagery, learning a shared image-GPS embedding space that is used to retrieve candidate locations. An attention-based reranker then rescores retrieved candidates by combining candidate-level visual and GPS features with cross-source evidence from nearby locations to ground the prediction. Unlike recent prior work, Pinpoint does not rely on multimodal large-language models, making inference faster and more reproducible. Pinpoint achieves state-of-the-art results across all metrics on standard benchmarks for internet photos (IM2GPS3k and YFCC4k) and street-view imagery (OSV-5M).

Subjects:

Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2606.04133 [cs.CV]

(or arXiv:2606.04133v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.04133

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Brian Hu [view email] [v1] Tue, 2 Jun 2026 18:44:58 UTC (3,489 KB)

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