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Semi Supervised Semantic Segmentation On 5

Métriques

Validation mIoU

Résultats

Résultats de performance de divers modèles sur ce 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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Semi Supervised Semantic Segmentation On 5 | SOTA | HyperAI