@inproceedings{
wang2024mfrgn,
title={{MFRGN}: Multi-scale Feature Representation Generalization Network For Ground-to-Aerial Geo-localization},
author={Yuntao Wang and Jinpu Zhang and Ruonan Wei and Wenbo Gao and Yuehuan Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=vaarOxGEU8}
}
Cross-area evaluation poses a significant challenge for ground-to-aerial geo-localization (G2AGL), in which the training and testing data are captured from entirely distinct areas.
However, current methods struggle in cross-area evaluation due to their emphasis solely on learning global information from single-scale features. Some efforts alleviate this problem but rely on complex and specific technologies like pre-processing and hard sample mining. To this end, we propose a pure end-to-end solution, free from task-specific techniques, termed the Multi-scale Feature Representation Generalization Network (MFRGN) to improve generalization. Specifically, we introduce multi-scale features and explicitly utilize them for G2GAL. Furthermore, we devise an efficient global-local information module with two flows to bolster feature representations. In the global flow, we present a lightweight Self and Cross Attention Module (SCAM) to efficiently learn global embeddings. In the local flow, we develop a Global-Prompt Attention Block (GPAB) to capture discriminative features under the global embeddings as prompts. 
As a result, our approach generates robust descriptors representing multi-scale global and local information, thereby enhancing the model's invariance to scene variations.
Extensive experiments on benchmarks show our MFRGN achieves competitive performance in same-area evaluation and improves cross-area generalization by a significant margin compared to SOTA methods.
MFRGN: Multi-scale Feature Representation Generalization Network For Ground-to-Aerial Geo-localization
Yuntao Wang Jinpu Zhang Ruonan Wei Wenbo Gao Yuehuan Wang