Semi Supervised Semantic Segmentation On 3

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

		Paper Title
SemiVL (ViT-B/16)	76.2	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
UniMatch (DeepLab v3+ with ResNet-101)	73.0	Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
CW-BASS (DeepLab v3+ with ResNet-50)	65.87	CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
SemiSegContrast (DeepLab v3+ with ResNet-50 backbone, MSCOCO pretrained)	64.9%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
SegSDE (MTL decoder with ResNet101, ImageNet pretrained, unlabeled image sequences)	62.09%	Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained)	60.28%	Bootstrapping Semantic Segmentation with Regional Contrast
SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained)	59.4%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)	58.70%	The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained)	56.9%	GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained)	56.53%	Bootstrapping Semantic Segmentation with Regional Contrast
DMT (DeepLab v2 MSCOCO/ImageNet pre-trained)	54.80%	DMT: Dynamic Mutual Training for Semi-Supervised Learning
ClassMix (DeepLab v2 MSCOCO pretrained)	54.07%	ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
CutMix (DeepLab v2, ImageNet pre-trained)	51.2	Semi-supervised semantic segmentation needs strong, varied perturbations

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Semi Supervised Semantic Segmentation On 3

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

		Paper Title
SemiVL (ViT-B/16)	76.2	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
UniMatch (DeepLab v3+ with ResNet-101)	73.0	Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
CW-BASS (DeepLab v3+ with ResNet-50)	65.87	CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
SemiSegContrast (DeepLab v3+ with ResNet-50 backbone, MSCOCO pretrained)	64.9%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
SegSDE (MTL decoder with ResNet101, ImageNet pretrained, unlabeled image sequences)	62.09%	Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
ReCo (DeepLab v3+ with ResNet-101 backbone, ImageNet pretrained)	60.28%	Bootstrapping Semantic Segmentation with Regional Contrast
SemiSegContrast (DeepLab v2 with ResNet-101 backbone, MSCOCO pretrained)	59.4%	Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank
GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)	58.70%	The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained)	56.9%	GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained)	56.53%	Bootstrapping Semantic Segmentation with Regional Contrast
DMT (DeepLab v2 MSCOCO/ImageNet pre-trained)	54.80%	DMT: Dynamic Mutual Training for Semi-Supervised Learning
ClassMix (DeepLab v2 MSCOCO pretrained)	54.07%	ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
CutMix (DeepLab v2, ImageNet pre-trained)	51.2	Semi-supervised semantic segmentation needs strong, varied perturbations

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Semi Supervised Semantic Segmentation On 3 | SOTA | HyperAI