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Few Shot Semantic Segmentation On Coco 20I

Metrics

Mean IoU

Results

Performance results of various models on this benchmark

Model Name
Mean IoU
Paper TitleRepository
FPTrans (DeiT-B/16)69.7Feature-Proxy Transformer for Few-Shot Segmentation-
HSNet (HM, ResNet-50)65.2HM: Hybrid Masking for Few-Shot Segmentation-
DGPNet (ResNet-101)70.1Dense Gaussian Processes for Few-Shot Segmentation-
HSNet (HM, ResNet-101)66.5HM: Hybrid Masking for Few-Shot Segmentation-
MSDNet (ResNet-101)73.9MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping-
VAT (HM, ResNet-50)65.1HM: Hybrid Masking for Few-Shot Segmentation-
RPMM49.6Prototype Mixture Models for Few-shot Semantic Segmentation-
CWT (ResNet-50)59.5Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer-
RePRI (ResNet-50)63.1Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?-
VAT + MSI (ResNet101)69.2MSI: Maximize Support-Set Information for Few-Shot Segmentation-
DGPNet (ResNet-50)68.9Dense Gaussian Processes for Few-Shot Segmentation-
MSDNet (ResNet-50)72.1MSDNet: Multi-Scale Decoder for Few-Shot Semantic Segmentation via Transformer-Guided Prototyping-
FPTrans (ViT-B/16)67.6Feature-Proxy Transformer for Few-Shot Segmentation-
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Few Shot Semantic Segmentation On Coco 20I | SOTA | HyperAI