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Adaptive Sparse Pairwise Loss for Object Re-Identification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace Triplet-BH with AdaSP | ReID metric learning · MSMT17 | mAP: 57.4 -> 60.7 | Helped |
| Replace Triplet-BH with AdaSP | ReID metric learning · Market-1501 | mAP: 79.4 -> 80.1 | Helped |
| Replace Triplet-BH with AdaSP | ReID metric learning · DukeMTMC-reID | mAP: 86.3 -> 86.8 | Helped |
| Replace Triplet-BH with AdaSP | ReID metric learning · VeRi-776 | mAP: 77.2 -> 77.6 | Helped |
| Use hardest positive mining instead of adaptive positive mining (ablation) | SP loss · MSMT17 | mAP: 60.7 -> 61.0 | Mixed |
| Use least-hard positive mining instead of adaptive positive mining (ablation) | SP loss · VeRi-776 | mAP: 77.6 -> 77.2 | Hurt |
| Use least-hard positive mining instead of adaptive positive mining (ablation) | SP loss · VehicleID Small | R1: 86.4 -> 87.0 | Helped |
| Increase instances per identity to 32 and use least-hard positive mining (ablation) | SP loss · MSMT17 | mAP | Helped |
| Increase instances per identity to 32 and use adaptive positive mining (ablation) | SP loss · MSMT17 | mAP | Helped |
| Set metric-loss weight λ to 0.1 (ablation) | AdaSP on MSMT17 · MSMT17 | mAP: 66.0 -> 67.1 | Helped |
| Set temperature τ to 0.04 (ablation) | AdaSP on MSMT17 · MSMT17 | mAP: 63.3 -> 67.1 | Helped |
| Vary metric-loss weight λ from 0.05 to 0.3 (ablation) | AdaSP on MSMT17 · MSMT17 | mAP: 66.8; 67.1; 67.0; 66.6 | No clear effect |
Closest papers by meaning
- TOP-ReID: Multi-Spectral Object Re-identification with Token Permutation (AAAI 2024 · similarity 0.86)
- Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification (CVPR 2025 · similarity 0.85)
- Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic (ICLR 2024 · similarity 0.85)
- Exploring the Camera Bias of Person Re-identification (ICLR 2025 · similarity 0.85)
- DeMo: Decoupled Feature-Based Mixture of Experts for Multi-Modal Object Re-Identification (AAAI 2025 · similarity 0.85)
- Catalyst for Clustering-Based Unsupervised Object Re-identification: Feature Calibration (AAAI 2024 · similarity 0.84)
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