Research map / Detection and segmentation / Open-set and few-shot detection
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Domain Diversification with one color-preservation shifted domain (ablation) | Faster R-CNN for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: 24.9 -> 31.2 | Helped |
| Add Multi-domain-invariant Representation Learning with one shifted domain (ablation) | DD for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: +1.2 | Helped |
| Add a reconstruction-constraint shifted domain alongside color preservation (ablation) | DD for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: 31.2 -> 32.5 | Helped |
| Add Multi-domain-invariant Representation Learning with two shifted domains (ablation) | DD for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: +5.3 | Helped |
| Add a third shifted domain trained with both constraints (ablation) | DD for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: 32.5 -> 33.8 | Helped |
| Add Multi-domain-invariant Representation Learning with three shifted domains (ablation) | DD for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: +8.0 | Helped |
| Add MRL to Domain Diversification (ablation) | Faster R-CNN for Real-world→Clipart1k · Real-world→Clipart1k test | classification accuracy: 50.2 -> 52.5 | Helped |
| Add MRL to Domain Diversification (ablation) | Faster R-CNN for Real-world→Clipart1k · Real-world→Clipart1k test | mIoU: 66.6 -> 68.5 | Helped |
| Use three diversified domains with MRL rather than the strongest listed baseline, DT | Faster R-CNN for Real-world→Clipart1k · Real-world→Clipart1k test | mAP: 32.1 -> 41.8 | Helped |
| Use three diversified domains with MRL rather than the strongest listed baseline, DT | Faster R-CNN for PASCAL VOC→Watercolor2k · PASCAL VOC→Watercolor2k test | mAP: 40.0 -> 52.0 | Helped |
| Use three diversified domains with MRL rather than the strongest listed baseline, DT | Faster R-CNN for PASCAL VOC→Comic2k · PASCAL VOC→Comic2k test | mAP: 23.5 -> 34.5 | Helped |
| Use three diversified domains with MRL rather than the strongest listed baseline, DT | Faster R-CNN for Cityscapes→Foggy Cityscapes · Cityscapes→Foggy Cityscapes validation | mAP: 31.5 -> 34.6 | Helped |
Closest papers by meaning
- Adapting Object Detectors via Selective Cross-Domain Alignment (CVPR 2019 · similarity 0.92)
- Domain Adaptive Faster R-CNN for Object Detection in the Wild (CVPR 2018 · similarity 0.91)
- DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection (NeurIPS 2024 · similarity 0.90)
- Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment (CVPR 2024 · similarity 0.89)
- Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment (AAAI 2025 · similarity 0.89)
- CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection (CVPR 2024 · similarity 0.89)
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