| UniMatch V2 (DINOv2-B) | 84.5% | UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation | |
| SemiVL (ViT-B/16) | 80.3% | SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance | |
| PrevMatch (ResNet-101) | 80.1% | Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation | |
| S4MC | 79.52% | Semi-Supervised Semantic Segmentation via Marginal Contextual Information | |
| Dual Teacher (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 79.46 | Switching Temporary Teachers for Semi-Supervised Semantic Segmentation | - |
| CorrMatch (Deeplabv3+ with ResNet-101) | 79.4% | CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation | |
| UniMatch (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 79.22% | Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation | |
| CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 79.21% | Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision | |
| AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 79.01% | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | |
| PrevMatch (ResNet-50) | 78.8% | Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation | |
| U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) | 78.51% | Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | |
| CW-BASS (DeepLab v3+ with ResNet-50) | 78.43% | CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation | |
| n-CPS (ResNet-50) | 78.41% | n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation | - |
| PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 78.4% | Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | |
| PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet-50, single scale inference) | 78.38% | Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation | |
| LaserMix (DeepLab v3+, ImageNet pre- trained ResNet50, single scale inference) | 78.3% | LaserMix for Semi-Supervised LiDAR Semantic Segmentation | |
| SimpleBaseline(DeepLabv3+ with ImageNet pretrained Xception65, single scale inference) | 77.8% | A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation | |
| CPCL (DeepLab v3+ with ResNet-50) | 76.98% | Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation | |
| Error Localization Network (DeeplabV3 with ResNet-50) | 73.52% | Semi-supervised Semantic Segmentation with Error Localization Network | |
| SegSDE (MTL decoder with ResNet101, ImageNet pretrained, unlabeled image sequences) | 69.38% | Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation | |