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Semi Supervised Semantic Segmentation On 1

Metrics

Validation mIoU

Results

Performance results of various models on this benchmark

Paper TitleRepository
UniMatch V2 (DINOv2-B)84.5%UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation
SemiVL (ViT-B/16)80.3%SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance
PrevMatch (ResNet-101)80.1%Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
S4MC79.52%Semi-Supervised Semantic Segmentation via Marginal Contextual Information
Dual Teacher (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)79.46Switching Temporary Teachers for Semi-Supervised Semantic Segmentation-
CorrMatch (Deeplabv3+ with ResNet-101)79.4%CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
UniMatch (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference)79.22%Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference)79.21%Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)79.01%Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
PrevMatch (ResNet-50)78.8%Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL)78.51%Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
CW-BASS (DeepLab v3+ with ResNet-50)78.43%CW-BASS: Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic Segmentation
n-CPS (ResNet-50)78.41%n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation-
PCR (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)78.4%Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
PS-MT (DeepLab v3+ with ImageNet-pretrained ResNet-50, single scale inference)78.38%Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
LaserMix (DeepLab v3+, ImageNet pre- trained ResNet50, single scale inference)78.3%LaserMix for Semi-Supervised LiDAR Semantic Segmentation
SimpleBaseline(DeepLabv3+ with ImageNet pretrained Xception65, single scale inference)77.8%A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
CPCL (DeepLab v3+ with ResNet-50)76.98%Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation
Error Localization Network (DeeplabV3 with ResNet-50)73.52%Semi-supervised Semantic Segmentation with Error Localization Network
SegSDE (MTL decoder with ResNet101, ImageNet pretrained, unlabeled image sequences)69.38%Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
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Semi Supervised Semantic Segmentation On 1 | SOTA | HyperAI