AFPN: Attention-guided Feature Partition Network for Cross-view Geo-localization

Cross-view geo-localization is to retrieve images of the same geographic target from different platforms. Since drones have received increasing attention in recent years because of their ability to capture high-quality multimedia data from the sky, we focus on image retrieval from the drone platform to the satellite platform in this paper. We propose an attention-guided feature partition network (AFPN) which leverages learnable spatial attention maps to divide the global high-level feature map into the class-aware foreground and the class-agnostic background feature in an end-to-end learning manner. Our backbone is based on the powerful vision transformer to model long-range global dependencies between patches. Data augmentation and multiple sampling strategies are also adopted in our experiments. Our method achieves Recall@1 accuracy at 95.60% on University-1652 and 94.48% on University-160k, and ranks 2nd in the ACMMM23 Multimedia Drone Satellite Matching Challenge.