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Deep Low-Resolution Person Re-Identification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| jointly learn SR and re-id (SING) vs separate SRCNN + DGD | DGD re-id · CAVIAR | rank-1 (%): 28.4 -> 33.5 | Helped |
| SING vs best prior LR re-id (JUDEA) | LR re-id · MLR-CUHK03 | rank-1 (%): 26.2 -> 67.7 | Helped |
| SING vs hand-crafted LOMO+XQDA/NFST | LR re-id · MLR-VIPeR | rank-1 (%): 39.7 vs 33.5 | Hurt |
| use SRCNN instead of bilinear upsampling before re-id | DGD · CAVIAR | rank-1 (%): 25.3 -> 28.4 | Helped |
| add synthetic LR stream with MSE SR loss (ablation) | SING · CAVIAR | rank-1 (%): 25.8 -> 33.5 | Helped |
| add synthetic LR stream with MSE SR loss (ablation) | SING · MLR-SYSU | rank-1 (%): 38.7 -> 50.7 | Helped |
| fuse three resolution-specific models vs single M1/2 (ablation) | SING · MLR-SYSU | rank-1 (%): 44.0 -> 50.7 | Helped |
| add SR loss (alpha=1 vs 0) (ablation) | SING · MLR-VIPeR | rank-1 (%): 25.0 -> 33.5 | Helped |
| large SR loss weight alpha > 1 (ablation) | SING · CAVIAR | rank-1 (%) | Hurt |
Closest papers by meaning
- Learning Resolution-Invariant Deep Representations for Person Re-Identification (AAAI 2019 · similarity 0.93)
- Inter-Task Association Critic for Cross-Resolution Person Re-Identification (CVPR 2020 · similarity 0.92)
- Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification (ICML 2026 · similarity 0.91)
- Deep Neural Networks with Inexact Matching for Person Re-Identification (NeurIPS 2016 · similarity 0.88)
- Resource Aware Person Re-Identification Across Multiple Resolutions (CVPR 2018 · similarity 0.88)
- Real-World Person Re-Identification via Degradation Invariance Learning (CVPR 2020 · similarity 0.87)
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