Image Based Localization On Cvact
Metrics
Recall@1
Recall@1 (%)
Recall@10
Recall@5
Results
Performance results of various models on this benchmark
Model Name | Recall@1 | Recall@1 (%) | Recall@10 | Recall@5 | Paper Title | Repository |
|---|---|---|---|---|---|---|
| LPN | 79.99 | 97.03 | 92.56 | 90.63 | Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization | |
| SAIG-D | 89.21 | 98.74 | 97.04 | 96.07 | Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization | |
| CV-Cities | 92.59 | 98.72 | 97.82 | 97.16 | CV-Cities: Advancing Cross-View Geo-Localization in Global Cities | |
| Transgeo | 84.95 | 98.37 | 95.78 | 94.14 | TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization | |
| RK-Net | 40.53 | 89.12 | - | - | Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization | |
| Sample4Geo | 90.81 | 98.77 | 97.48 | 96.74 | Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation | |
| GeoDTR | 86.21 | 98.77 | 96.72 | 95.44 | Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials | - |
| Instance Loss | 35.24 | 87.34 | - | - | Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization |
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