Semi Supervised Semantic Segmentation On 4

Métriques

Validation mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Validation mIoU	Paper Title	Repository
GIST and RIST	70.76%	The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation	-
Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval)	65.82%	Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks	-
CW-BASS (DeepLab v3+ with ResNet-50)	75.81%	CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation	-
ClassMix	71.00%	ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning	-
SemiSegContrast	71.6%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank	-
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, CutMix)	79.01%	Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels	-
PS-MT	75.70%	Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation	-
GuidedMix-Net	76.4%	GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference	-
PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)	80.71%	Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization	-
s4GAN + MLMT	71.4%	Semi-Supervised Semantic Segmentation with High- and Low-level Consistency	-
CPCL (DeepLab v3+ with ResNet-50)	73.74%	Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation	-
CPCL (DeepLab v3+ with ResNet-101)	76.4%	Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation	-
Error Localization Network	75.10%	Semi-supervised Semantic Segmentation with Error Localization Network	-
S4MC	79.67%	Semi-Supervised Semantic Segmentation via Marginal Contextual Information	-
CutMix	72.45%	Semi-supervised semantic segmentation needs strong, varied perturbations	-
Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval)	62.49%	Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks	-
ReCo	71.00%	Bootstrapping Semantic Segmentation with Regional Contrast	-
CPS	76.44%	Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision	-
s4GAN+MLMT	70.4%	Semi-Supervised Semantic Segmentation with High- and Low-level Consistency	-
AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)	77.57%	Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning	-

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