Cross Domain Few Shot Object Detection On 2
Metrics
mAP
Results
Performance results of various models on this benchmark
Model Name | mAP | Paper Title | Repository |
|---|---|---|---|
| FSCE | 21.9 | FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding | |
| Detic-FT | 15.4 | Detecting Twenty-thousand Classes using Image-level Supervision | |
| ViTDeT-FT | 29.4 | Exploring Plain Vision Transformer Backbones for Object Detection | |
| BIOT | 31.1 | Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners | - |
| TFA w/cos | 20.5 | Frustratingly Simple Few-Shot Object Detection | |
| CD-ViTO | 30.8 | Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector | |
| DeFRCN | 22.9 | DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection | |
| DE-ViT-FT | 25.6 | Detect Everything with Few Examples | |
| Meta-RCNN | 20.6 | Meta-RCNN: Meta Learning for Few-Shot Object Detection | - |
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