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Viewpoint-Aware Attentive Multi-View Inference for Vehicle Re-Identification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Infer multi-view features instead of using original single-view features (ablation) | VAMI · VeRi-776 | mAP: 28.64 -> 41.94 | Helped |
| Apply pairwise metric learning to inferred multi-view features (ablation) | VAMI · VeRi-776 | mAP: 41.94 -> 50.13 | Helped |
| Add viewpoint-aware attention (ablation) | VAMI · VeRi-776 | mAP: 40.12 -> 50.13 | Helped |
| Replace feature matching with the regular generator objective (ablation) | VAMI · VeRi-776 | mAP: 50.13 -> 41.59 | Hurt |
| Remove auxiliary vehicle classifiers from D (ablation) | VAMI · VeRi-776 | mAP: 50.13 -> 33.43 | Hurt |
| Replace residual transformation modules with regular CNNs (ablation) | VAMI · VeRi-776 | mAP: 50.13 -> 42.89 | Hurt |
| Replace adversarial loss with ℓ2 loss (ablation) | VAMI · VeRi-776 | mAP: 50.13 -> 34.96 | Hurt |
| Set attention depth to k=1, k=3, or k=4 instead of k=2 (ablation) | VAMI · VeRi-776 | mAP: 50.13; 43.78, 45.60, 41.05 | Hurt |
| Set dropout rate to n=0.1, 0.2, 0.4, 0.5, or 0.6 instead of n=0.3 (ablation) | VAMI · VeRi-776 | mAP: 50.13; 42.33, 43.25, 47.68, 45.54, 41.59 | Hurt |
| Add spatial-temporal relations | VAMI · VeRi-776 | mAP: 50.13 -> 61.32 | Helped |
| Use VAMI instead of the strongest listed vision-based baseline | Vehicle re-identification · VehicleID | rank-1: 52.87 -> 63.12; 49.55 -> 52.87; 44.89 -> 47.34 | Helped |
Closest papers by meaning
- Parsing-Based View-Aware Embedding Network for Vehicle Re-Identification (CVPR 2020 · similarity 0.89)
- Infer the Whole from a Glimpse of a Part: Keypoint-Based Knowledge Graph for Vehicle Re-Identification (AAAI 2025 · similarity 0.88)
- CLIP-driven View-aware Prompt Learning for Unsupervised Vehicle Re-identification (AAAI 2025 · similarity 0.87)
- Structural Information Guided Multimodal Pre-training for Vehicle-Centric Perception (AAAI 2024 · similarity 0.83)
- Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic (ICLR 2024 · similarity 0.82)
- M-BEV: Masked BEV Perception for Robust Autonomous Driving (AAAI 2024 · similarity 0.82)
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