Research map / Image generation / Autoregressive and latent generation
VIGOR: Cross-View Image Geo-Localization Beyond One-to-One Retrieval
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the hybrid loss with IOU-based semi-positive supervision and offset prediction instead of triplet loss alone | SAFA+Mining on VIGOR · VIGOR same-area | Top-1 retrieval accuracy (%): 38.0 -> 41.1 | Helped |
| Use the hybrid loss with IOU-based semi-positive supervision and offset prediction instead of triplet loss alone | SAFA+Mining on VIGOR · VIGOR cross-area | Top-1 retrieval accuracy (%): 9.2 -> 11.0 | Helped |
| Add IOU-based semi-positive assignment (ablation) | Triplet-loss baseline · VIGOR same-area | Top-1 retrieval accuracy (%): 38.0 -> 41.1 | Helped |
| Add IOU-based semi-positive assignment (ablation) | Triplet-loss baseline · VIGOR cross-area | Top-1 retrieval accuracy (%): 9.2 -> 10.7 | Helped |
| Assign semi-positive samples as positives (ablation) | Triplet-loss baseline · VIGOR same-area | Top-1 retrieval accuracy (%): 38.0 -> 20.3 | Hurt |
| Assign semi-positive samples as positives (ablation) | Triplet-loss baseline · VIGOR cross-area | Top-1 retrieval accuracy (%): 9.2 -> 2.7 | Hurt |
| Add positive assignment alongside the IOU-based loss (ablation) | IOU semi-positive assignment method · VIGOR same-area | Top-1 retrieval accuracy (%): 41.1 -> 31.1 | Hurt |
| Add positive assignment alongside the IOU-based loss (ablation) | IOU semi-positive assignment method · VIGOR cross-area | Top-1 retrieval accuracy (%): 10.7 -> 5.3 | Hurt |
| Add offset regression (ablation) | VIGOR retrieval framework · VIGOR same-area | 10-meter localization accuracy: 11.4% -> 25.5% | Helped |
| Replace offset regression with grid classification (ablation) | VIGOR localization framework · VIGOR cross-area | 10-meter localization accuracy | Hurt |
| Increase positive panoramas per aerial image from 2 to 3 (ablation) | VIGOR · VIGOR same-area | Top-1 retrieval accuracy (%): 41.1 -> 48.5 | Helped |
| Increase positive panoramas per aerial image from 2 to 3 (ablation) | VIGOR · VIGOR cross-area | Top-1 retrieval accuracy (%): 10.7 -> 11.5 | Helped |
Closest papers by meaning
- From Coarse to Fine: A Matching and Alignment Framework for Unsupervised Cross-View Geo-Localization (AAAI 2025 · similarity 0.89)
- Aligning Geometric Spatial Layout in Cross-View Geo-Localization via Feature Recombination (AAAI 2024 · similarity 0.89)
- GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization (NeurIPS 2025 · similarity 0.88)
- Semantic Ambiguity Modeling and Propagation for Fine-Grained Visual Cross View Geo-Localization (AAAI 2025 · similarity 0.87)
- GeoFidelity-Bench: Evaluating Block-Conditioned Geographic Fidelity of Street-View Generation (NeurIPS 2026 · similarity 0.87)
- Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization (CVPR 2024 · similarity 0.86)
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