Research map / Detection and segmentation / Open-set and few-shot detection
Multi-View Domain Adaptive Object Detection on Camera Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the two-stage framework: multi-view self-supervised backbone pre-training followed by detection-head self-training | YOLOv3 · WildTrack | mAP (%): 65.41 -> 72.50 | Helped |
| Use the two-stage framework: multi-view self-supervised backbone pre-training followed by detection-head self-training | YOLOv3 · CityFlow | mAP (%): 62.27 -> 69.49 | Helped |
| Add multi-view self-supervised backbone pre-training before detection-head self-training (ablation) | YOLOv3 · WildTrack | mAP (%): 58.23 -> 69.84 | Helped |
| Add multi-view self-supervised backbone pre-training before detection-head self-training (ablation) | YOLOv3 · CityFlow | mAP (%): 55.12 -> 71.22 | Helped |
| Use self-supervised backbone pre-training and train only the detection head in stage 2 (ablation) | YOLOv3 · WildTrack | mAP (%): 66.54 -> 72.50 | Helped |
| Use self-supervised backbone pre-training and train only the detection head in stage 2 (ablation) | YOLOv3 · CityFlow | mAP (%): 63.92 -> 69.49 | Helped |
| Add epipolar constraints to prune candidate boxes before reID matching (ablation) | Multi-view self-supervised association · WildTrack | mAP (%): 69.84 -> 72.50 | Helped |
| Add epipolar constraints to prune candidate boxes before reID matching (ablation) | Multi-view self-supervised association · CityFlow | mAP (%): 71.22 -> 69.49 | Hurt |
| Add tracking-based viewpoint augmentation for weak-hard consistency training (ablation) | YOLOv3 robust self-training · WildTrack | mAP (%): 65.41 -> 72.50 | Helped |
| Add tracking-based viewpoint augmentation for weak-hard consistency training (ablation) | YOLOv3 robust self-training · CityFlow | mAP (%): 62.15 -> 69.49 | Helped |
| Replace generic image augmentation with tracking-based viewpoint augmentation (ablation) | YOLOv3 robust self-training · WildTrack | mAP (%): 66.28 -> 72.50 | Helped |
| Replace generic image augmentation with tracking-based viewpoint augmentation (ablation) | YOLOv3 robust self-training · CityFlow | mAP (%): 64.56 -> 69.49 | Helped |
Closest papers by meaning
- Unified Domain Generalization and Adaptation for Multi-View 3D Object Detection (NeurIPS 2024 · similarity 0.88)
- CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection (CVPR 2024 · similarity 0.88)
- DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection (NeurIPS 2024 · similarity 0.88)
- Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment (CVPR 2024 · similarity 0.87)
- Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection (CVPR 2025 · similarity 0.87)
- SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection (CVPR 2025 · similarity 0.87)
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