| UniMatch V2 (DINOv2-B) | 84.3% | UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation | |
| SemiVL (ViT-B/16) | 79.4% | SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance | |
| PrevMatch (ResNet-101) | 78.9% | Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation | |
| CorrMatch (Deeplabv3+ with ResNet-101) | 78.5% | CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation | |
| Dual Teacher | 78.4 | Switching Temporary Teachers for Semi-Supervised Semantic Segmentation | - |
| UniMatch (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 77.92% | Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation | |
| AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 77.9% | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | |
| PrevMatch (ResNet-50) | 77.8% | Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation | |
| S4MC | 77.78% | Semi-Supervised Semantic Segmentation via Marginal Contextual Information | |
| CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 77.62% | Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision | |
| n-CPS (ResNet-50) | 77.61% | n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation | - |
| CW-BASS (DeepLab v3+ with ResNet-50) | 77.20% | CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation | |
| PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet50, single scale inference) | 77.12% | Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation | |
| LaserMix (DeepLab v3+, ImageNet pre-trained ResNet50, single scale inference) | 77.1% | LaserMix for Semi-Supervised LiDAR Semantic Segmentation | |
| U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) | 76.48% | Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | |
| PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 76.31% | Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | |
| CPCL (DeepLab v3+ with ResNet-50) | 74.6% | Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation | |
| SimpleBaseline(DeeplabV3+ with ImageNet pretrained Xception65, sinle scale inference) | 74.1% | A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation | |
| Dense FixMatch (DeepLabv3+ ResNet-101, uniform sampling, single pass eval) | 73.91% | Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks | |
| Dense FixMatch (DeepLabv3+ ResNet-50, uniform sampling, single pass eval) | 73.39% | Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks | |