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Cross Domain Few Shot Object Detection On

Metrics

mAP

Results

Performance results of various models on this benchmark

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

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Cross Domain Few Shot Object Detection On | SOTA | HyperAI