Semi Supervised Semantic Segmentation On 5

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

		Paper Title
ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained)	73.66%	Bootstrapping Semantic Segmentation with Regional Contrast
Error Localization Network (DeeplabV3 with ResNet-101)	72.52%	Semi-supervised Semantic Segmentation with Error Localization Network
Error Localization Network (DeeplabV3 with ResNet-50)	70.52%	Semi-supervised Semantic Segmentation with Error Localization Network
SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained)	70.0%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
DMT (DeepLab v2 MSCOCO/ImageNet pre-trained)	69.92%	DMT: Dynamic Mutual Training for Semi-Supervised Learning
CutMix (DeepLab v3+ ImageNet pre-trained)	69.57%	Semi-supervised semantic segmentation needs strong, varied perturbations
GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)	69.40%	The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained)	68.85%	Bootstrapping Semantic Segmentation with Regional Contrast
ClassMix (DeepLab v2 MSCOCO pretrained)	67.77%	ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
s4GAN + MLMT (DeepLab v2 MSCOCO/ImageNet pre-trained)	67.2%	Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
s4GAN+MLMT (DeepLab v3+ ImageNet pre-trained)	66.6%	Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
CutMix (DeepLab v2 ImageNet pre-trained)	66.48%	Semi-supervised semantic segmentation needs strong, varied perturbations
s4GAN+MLMT (DeepLab v2 ImageNet pre-trained)	62.9%	Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
Adversarial (DeepLab v2 ImageNet pre-trained)	59.1%	Adversarial Learning for Semi-Supervised Semantic Segmentation

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