A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios
Zhuo Song, Ye Zhang, Kunhong Li, Longguang Wang, Yulan Guo

TL;DR
UnifyGeo is a unified hierarchical framework that integrates retrieval and metric localization for large-scale, fine-grained geo-localization, significantly improving accuracy and efficiency over previous methods.
Contribution
The paper introduces UnifyGeo, a novel framework that jointly learns multi-granularity representations and employs a re-ranking mechanism, unifying two key localization tasks into one network.
Findings
Substantially outperforms state-of-the-art methods on VIGOR benchmark.
Improves 1-meter localization recall from 1.53% to 39.64%.
Enhances cross-area localization recall from 0.43% to 25.58%.
Abstract
Cross-view geo-localization is a promising solution for large-scale localization problems, requiring the sequential execution of retrieval and metric localization tasks to achieve fine-grained predictions. However, existing methods typically focus on designing standalone models for these two tasks, resulting in inefficient collaboration and increased training overhead. In this paper, we propose UnifyGeo, a novel unified hierarchical geo-localization framework that integrates retrieval and metric localization tasks into a single network. Specifically, we first employ a unified learning strategy with shared parameters to jointly learn multi-granularity representation, facilitating mutual reinforcement between these two tasks. Subsequently, we design a re-ranking mechanism guided by a dedicated loss function, which enhances geo-localization performance by improving both retrieval accuracy…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization · Multimodal Machine Learning Applications
MethodsFocus
