Cross-View Image Set Geo-Localization
Qiong Wu, Panwang Xia, Lei Yu, Yi Liu, Mingtao Xiong, Liheng Zhong,, Jingdong Chen, Ming Yang, Yongjun Zhang, Yi Wan

TL;DR
This paper introduces a new cross-view image set geo-localization task, a large benchmark dataset, and a flexible method that improves localization accuracy by integrating multiple perspectives and geo-attributes.
Contribution
The paper proposes the novel Set-CVGL task, creates the extensive SetVL-480K benchmark, and introduces FlexGeo, a versatile method that enhances geo-localization by fusing multi-view features and leveraging geo-attributes.
Findings
FlexGeo outperforms existing methods on multiple datasets.
Localization accuracy improves by over 22% on SetVL-480K.
The method effectively integrates diverse perspectives for better localization.
Abstract
Cross-view geo-localization (CVGL) has been widely applied in fields such as robotic navigation and augmented reality. Existing approaches primarily use single images or fixed-view image sequences as queries, which limits perspective diversity. In contrast, when humans determine their location visually, they typically move around to gather multiple perspectives. This behavior suggests that integrating diverse visual cues can improve geo-localization reliability. Therefore, we propose a novel task: Cross-View Image Set Geo-Localization (Set-CVGL), which gathers multiple images with diverse perspectives as a query set for localization. To support this task, we introduce SetVL-480K, a benchmark comprising 480,000 ground images captured worldwide and their corresponding satellite images, with each satellite image corresponds to an average of 40 ground images from varied perspectives and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Image Segmentation Techniques · Image Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques
MethodsSparse Evolutionary Training
