| PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 80.78 | Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | |
| UniMatch (DeepLab v3+ with ResNet-101) | 80.43 | Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation | |
| U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, CutMix) | 79.3 | Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | |
| PS-MT
(DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 78.72 | Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation | |
| AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 78.06 | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | |
| GuidedMix-Net(DeepLab v2 with ResNet101, input-size: 512x512 with multi-scale and flip, ImageNet pretrained) | 77.8% | GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference | |
| CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference) | 77.68% | Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision | |
| Error Localization Network (DeeplabV3 with ResNet-101) | 76.58% | Semi-supervised Semantic Segmentation with Error Localization Network | |
| PCT (DeepLab v3+ with ResNet-50 pretrained on ImageNet-1K) | 76.47 | Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation | - |
| GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained) | 75.5% | GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference | |
| Error Localization Network (DeeplabV3 with ResNet-50) | 74.63% | Semi-supervised Semantic Segmentation with Error Localization Network | |