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Semi Supervised Semantic Segmentation On 2

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Validation mIoU

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이 벤치마크에서 각 모델의 성능 결과

Paper Title
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 Teacher78.4Switching 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
S4MC77.78%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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