Cross Domain Few Shot Object Detection On
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mAP
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이 벤치마크에서 각 모델의 성능 결과
모델 이름 | mAP | Paper Title | Repository |
|---|---|---|---|
| Detic-FT | 12.0 | Detecting Twenty-thousand Classes using Image-level Supervision | - |
| TFA w/cos | 14.8 | Frustratingly Simple Few-Shot Object Detection | - |
| DeFRCN | 15.5 | DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection | - |
| ViTDeT-FT | 23.4 | Exploring Plain Vision Transformer Backbones for Object Detection | - |
| CD-ViTO | 60.5 | Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector | - |
| BIOT(5-Shot) | 53.3 | Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners | - |
| FSCE | 15.9 | FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding | - |
| Meta-RCNN | 14.0 | Meta-RCNN: Meta Learning for Few-Shot Object Detection | - |
| DE-ViT-FT | 49.2 | Detect Everything with Few Examples | - |
| BIOT(10-Shot) | 58.4 | Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners | - |
0 of 10 row(s) selected.