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Semi Supervised Semantic Segmentation On 15

평가 지표

Validation mIoU

평가 결과

이 벤치마크에서 각 모델의 성능 결과

Paper Title
S4MC81.11Semi-Supervised Semantic Segmentation via Marginal Contextual Information
PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)80.91%Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, CutMix)80.5%Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)80.29%Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
n-CPS (ResNet-101)80.26%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)79.76%Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
GuidedMix-Net(DeepLab v2 with ResNet101, input-size: 512x512 with multi-scale and flip, ImageNet pretrained)78.2%GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
CPCL (DeepLab v3+ with ResNet-101)77.67%Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
PCT (DeepLab v3+ with ResNet-50 pretrained on ImageNet-1K)77.26%Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation
n-CPS (ResNet-50)77.07%n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation
GuidedMix-Net(DeepLab v2 with ResNet101, ImageNet pretrained)76.5%GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference
CPCL (DeepLab v3+ with ResNet-50)75.3%Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Dense FixMatch (DeepLabv3+ ResNet-101, over-sampling, single pass eval)74.73%Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
Dense FixMatch (DeepLabv3+ ResNet-50, over-sampling, single pass eval)71.69%Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
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