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SOTA
半监督语义分割
Semi Supervised Semantic Segmentation On 2
Semi Supervised Semantic Segmentation On 2
评估指标
Validation mIoU
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Validation mIoU
Paper Title
Repository
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
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