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SOTA
Halbüberwachte Semantische Segmentierung
Semi Supervised Semantic Segmentation On 4
Semi Supervised Semantic Segmentation On 4
Metriken
Validation mIoU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Validation mIoU
Paper Title
AllSpark
82.04%
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
UniMatch
81.92%
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
PrevMatch (ResNet-101)
81.9
Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
CorrMatch (Deeplabv3+ with ResNet-101)
81.9%
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
Dual Teacher
81.19
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)
80.71%
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
S4MC
79.67%
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, CutMix)
79.01%
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
PS-MT
78.20%
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
n-CPS (ResNet-101)
77.99%
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)
77.57%
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
CPS
76.44%
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
GuidedMix-Net
76.4%
GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
CPCL (DeepLab v3+ with ResNet-101)
76.4%
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
CW-BASS (DeepLab v3+ with ResNet-50)
75.81%
CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
PS-MT
75.70%
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
PCT (DeepLab v3+ with ResNet-50 pretrained on ImageNet-1K)
75.52%
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation
Error Localization Network
75.10%
Semi-supervised Semantic Segmentation with Error Localization Network
n-CPS
74.21%
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
CPCL (DeepLab v3+ with ResNet-50)
73.74%
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
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