Cross Domain Few Shot Object Detection On 4

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
BIOT(10-shot)	20.4	Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
BIOT(5-shot)	18.0	Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
Detic-FT	16.8	Detecting Twenty-thousand Classes using Image-level Supervision
ViTDeT-FT	15.8	Exploring Plain Vision Transformer Backbones for Object Detection
DeFRCN	12.1	DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
FSCE	12.0	FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
TFA w/cos	11.8	Frustratingly Simple Few-Shot Object Detection
Meta-RCNN	11.2	Meta-RCNN: Meta Learning for Few-Shot Object Detection
CD-ViTO	7.0	Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
DE-ViT-FT	5.4	Detect Everything with Few Examples

0 of 10 row(s) selected.

Cross Domain Few Shot Object Detection On 4

Metrics

mAP

Results

Performance results of various models on this benchmark

		Paper Title
BIOT(10-shot)	20.4	Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
BIOT(5-shot)	18.0	Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
Detic-FT	16.8	Detecting Twenty-thousand Classes using Image-level Supervision
ViTDeT-FT	15.8	Exploring Plain Vision Transformer Backbones for Object Detection
DeFRCN	12.1	DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
FSCE	12.0	FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding
TFA w/cos	11.8	Frustratingly Simple Few-Shot Object Detection
Meta-RCNN	11.2	Meta-RCNN: Meta Learning for Few-Shot Object Detection
CD-ViTO	7.0	Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
DE-ViT-FT	5.4	Detect Everything with Few Examples

0 of 10 row(s) selected.

Cross Domain Few Shot Object Detection On 4 | SOTA | HyperAI