Learnable Query Aggregation with KV Routing for Cross-view Geo-localisation
Hualin Ye, Bingxi Liu, Jixiang Du, Yu Qin, Ziyi Chen, Hong Zhang

TL;DR
This paper introduces a novel cross-view geo-localisation system that enhances feature aggregation and alignment through a DINOv2 backbone, multi-scale channel reallocation, and a Mixture-of-Experts routing mechanism, achieving competitive results.
Contribution
The paper presents a new CVGL approach with a learnable query aggregation method using MoE routing, improving adaptability and efficiency over previous methods.
Findings
Achieves competitive performance on University-1652 and SUES-200 datasets.
Uses fewer trained parameters than existing methods.
Demonstrates improved feature diversity and stability.
Abstract
Cross-view geo-localisation (CVGL) aims to estimate the geographic location of a query image by matching it with images from a large-scale database. However, the significant view-point discrepancies present considerable challenges for effective feature aggregation and alignment. To address these challenges, we propose a novel CVGL system that incorporates three key improvements. Firstly, we leverage the DINOv2 backbone with a convolution adapter fine-tuning to enhance model adaptability to cross-view variations. Secondly, we propose a multi-scale channel reallocation module to strengthen the diversity and stability of spatial representations. Finally, we propose an improved aggregation module that integrates a Mixture-of-Experts (MoE) routing into the feature aggregation process. Specifically, the module dynamically selects expert subspaces for the keys and values in a cross-attention…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Advanced Neural Network Applications · Robotics and Sensor-Based Localization
