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المنصة
الرئيسية
SOTA
التمييز_الدلالي_شبه_المشرف
Semi Supervised Semantic Segmentation On 15
Semi Supervised Semantic Segmentation On 15
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Validation mIoU
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نتائج أداء النماذج المختلفة على هذا المعيار القياسي
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اسم النموذج
Validation mIoU
Paper Title
S4MC
81.11
Semi-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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