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Semi Supervised Semantic Segmentation On 6

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

Paper Title
ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained)72.14%Bootstrapping Semantic Segmentation with Regional Contrast
SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained)67.9%Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)67.21%The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
DMT (DeepLab v2 MSCOCO pre-trained)67.15%DMT: Dynamic Mutual Training for Semi-Supervised Learning
CutMix (DeepLab v3+ ImageNet pre-trained)67.05%Semi-supervised semantic segmentation needs strong, varied perturbations
ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained)66.41%Bootstrapping Semantic Segmentation with Regional Contrast
ClassMix (DeepLab v2 MSCOCO pretrained)66.15%ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
CutMix (DeepLab v2 ImageNet pre-trained)64.81%Semi-supervised semantic segmentation needs strong, varied perturbations
s4GAN + MLMT (DeepLab v2 MSCOCO/ImageNet pre-trained)63.3%Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
s4GAN+MLMT (DeepLab v3+ ImageNet pre-trained)62.6%Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
s4GAN+MLMT (DeepLab v2 ImageNet pre-trained)60.4%Semi-Supervised Semantic Segmentation with High- and Low-level Consistency
Adversarial (DeepLab v2 ImageNet pre-trained)49.2%Adversarial Learning for Semi-Supervised Semantic Segmentation
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Semi Supervised Semantic Segmentation On 6 | SOTA | HyperAI