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Drone View Target Localization On University 1

Metrics

AP
Recall@1

Results

Performance results of various models on this benchmark

Paper TitleRepository
Orientation-Guided Sample4Geo96.8896.13Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation-
CV-Cities95.0197.43CV-Cities: Advancing Cross-View Geo-Localization in Global Cities
Sample4Geo93.8192.65Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation
Cross-view Consistent Attention93.3191.57A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention-
FSRA84.8282.25A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
LPN + USAM80.5577.60Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization-
LPN79.1475.93Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
SAFA + USAM75.7972.19Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization-
GNN-Reranking74.1170.3Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
RK-Net70.2366.13Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization-
Instance Loss63.1358.49University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
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Drone View Target Localization On University 1 | SOTA | HyperAI