Learnable Query Aggregation for Cross-view Geo-localisation
Published:Dec 30, 2025 01:51
•1 min read
•ArXiv
Analysis
This paper addresses the challenging problem of cross-view geo-localisation, which is crucial for applications like autonomous navigation and robotics. The core contribution lies in the novel aggregation module that uses a Mixture-of-Experts (MoE) routing mechanism within a cross-attention framework. This allows for adaptive processing of heterogeneous input domains, improving the matching of query images with a large-scale database despite significant viewpoint discrepancies. The use of DINOv2 and a multi-scale channel reallocation module further enhances the system's performance. The paper's focus on efficiency (fewer trained parameters) is also a significant advantage.
Key Takeaways
Reference
“The paper proposes an improved aggregation module that integrates a Mixture-of-Experts (MoE) routing into the feature aggregation process.”