Cross Domain Few Shot Object Detection On 4
Metrics
mAP
Results
Performance results of various models on this benchmark
Model Name | mAP | Paper Title | Repository |
|---|---|---|---|
| FSCE | 12.0 | FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding | |
| DeFRCN | 12.1 | DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection | |
| Detic-FT | 16.8 | Detecting Twenty-thousand Classes using Image-level Supervision | |
| TFA w/cos | 11.8 | Frustratingly Simple Few-Shot Object Detection | |
| Meta-RCNN | 11.2 | Meta-RCNN: Meta Learning for Few-Shot Object Detection | - |
| BIOT(5-shot) | 18.0 | Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners | - |
| CD-ViTO | 7.0 | Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector | |
| ViTDeT-FT | 15.8 | Exploring Plain Vision Transformer Backbones for Object Detection | |
| DE-ViT-FT | 5.4 | Detect Everything with Few Examples | |
| BIOT(10-shot) | 20.4 | Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners | - |
0 of 10 row(s) selected.