| Error Localization Network (DeeplabV3 with ResNet-50) | 75.33% | Semi-supervised Semantic Segmentation with Error Localization Network | |
| PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet-50, single scale inference) | 79.22% | Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation | |
| Adversarial (DeepLab v2 ImageNet pre-trained) | 65.70% | Adversarial Learning for Semi-Supervised Semantic Segmentation | |
| ClassMix (DeepLab v2 MSCOCO pretrained) | 66.29% | ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning | |
| LaserMix (DeepLab v3+, ImageNet pre- trained ResNet50, single scale inference) | 79.1% | LaserMix for Semi-Supervised LiDAR Semantic Segmentation | |
| AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 80.28% | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | |
| ReCo (DeepLab v2 with ResNet-101 backbone, ImageNet pretrained) | 68.69% | Bootstrapping Semantic Segmentation with Regional Contrast | |
| U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) | 79.12% | Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | |
| PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 79.11% | Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | |
| GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) | 69.8% | GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference | |
| SimpleBaseline(DeepLabv3+ with ImageNet pretrained Xception65, single scale inference) | 78.7% | A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation | |