Semi Supervised Semantic Segmentation On 30

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

		Paper Title
UniMatch V2 (DINOv2-B)	90.0	UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
SemiVL (ViT-B/16)	86.7	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
UniMatch (ViT-B/16)	83.3	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
AllSpark	80.75	AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
CorrMatch (Deeplabv3+ with ResNet-101)	80.6	CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
PrevMatch (ResNet-101)	80.4	Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
S4MC	80.12	Semi-Supervised Semantic Segmentation via Marginal Contextual Information
UniMatch (DeepLab v3+ with ResNet-101)	79.90	Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
LogicDiag (DeepLab v3+ with ResNet-101)	79.4	Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
PrevMatch (ResNet-50)	78.6	Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
S4MC (R50)	77.83	Semi-Supervised Semantic Segmentation via Marginal Contextual Information
U2PL (DeepLab v3+ with ResNet-101)	76.2	Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
CPS (DeepLab v3+ with ResNet-101)	75.9	Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
CPCL (DeepLab v3+ with ResNet-50)	74.25	Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

0 of 14 row(s) selected.

Semi Supervised Semantic Segmentation On 30

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

		Paper Title
UniMatch V2 (DINOv2-B)	90.0	UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
SemiVL (ViT-B/16)	86.7	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
UniMatch (ViT-B/16)	83.3	SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
AllSpark	80.75	AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
CorrMatch (Deeplabv3+ with ResNet-101)	80.6	CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
PrevMatch (ResNet-101)	80.4	Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
S4MC	80.12	Semi-Supervised Semantic Segmentation via Marginal Contextual Information
UniMatch (DeepLab v3+ with ResNet-101)	79.90	Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
LogicDiag (DeepLab v3+ with ResNet-101)	79.4	Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
PrevMatch (ResNet-50)	78.6	Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
S4MC (R50)	77.83	Semi-Supervised Semantic Segmentation via Marginal Contextual Information
U2PL (DeepLab v3+ with ResNet-101)	76.2	Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
CPS (DeepLab v3+ with ResNet-101)	75.9	Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
CPCL (DeepLab v3+ with ResNet-50)	74.25	Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation

0 of 14 row(s) selected.