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プラットフォーム
ホーム
SOTA
半教師付きセマンティックセグメンテーション
Semi Supervised Semantic Segmentation On 9
Semi Supervised Semantic Segmentation On 9
評価指標
Validation mIoU
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
Columns
モデル名
Validation mIoU
Paper Title
Dual Teacher
81.03
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
AllSpark
80.92
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
CorrMatch (Deeplabv3+ with ResNet-101)
80.9
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
PrevMatch (ResNet-101)
80.8
Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
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
S4MC
79.85
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, CutMix)
79.3
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
n-CPS (ResNet-101)
78.97
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
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
CPCL (DeepLab v3+ with ResNet-101)
77.16
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
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
CW-BASS (DeepLab v3+ with ResNet-50)
76.2
CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
n-CPS (ResNet-50)
75.85
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-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
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