| U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL) | 74.90% | Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels | |
| PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 73.41% | Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization | |
| Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval) | 71.1% | Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks | |
| Dual Teacher (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 76.81 | Switching Temporary Teachers for Semi-Supervised Semantic Segmentation | |
| CPS (DeepLab v3+ with ResNet-101) | 69.8 | Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision | |
| AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 75.83% | Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning | |
| UniMatch (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | 76.59 | Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation | |
| Dense FixMatch (DeepLabv3+ ResNet-50, uniform sampling, single pass eval) | 70.65% | Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks | |