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Semi Supervised Semantic Segmentation On 2

المقاييس

Validation mIoU

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
Validation mIoU
Paper TitleRepository
DMT (DeepLab v2 MSCOCO/ImageNet pre-trained)63.03%DMT: Dynamic Mutual Training for Semi-Supervised Learning-
UniMatch V2 (DINOv2-B)84.3%UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation-
PrevMatch (ResNet-101)78.9%Revisiting and Maximizing Temporal Knowledge in 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-
U2PL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K, AEL)76.48%Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels-
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-
Error Localization Network (DeeplabV3 with ResNet-50)70.33%Semi-supervised Semantic Segmentation with Error Localization Network-
AEL (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K)77.9%Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning-
SimpleBaseline(DeeplabV3+ with ImageNet pretrained Xception65, sinle scale inference)74.1%A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation-
CutMix (DeepLab v2, ImageNet pre-trained)60.34%Semi-supervised semantic segmentation needs strong, varied perturbations-
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-
GIST and RIST (DeepLabv2 with ResNet101, MSCOCO pre-trained)62.57%The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation-
CPS (DeepLab v3+ with ImageNet-pretrained ResNet-101, single scale inference)77.62%Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision-
ClassMix (DeepLab v2 MSCOCO pretrained)61.35%ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning-
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-
SemiVL (ViT-B/16)79.4%SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language Guidance-
CPCL (DeepLab v3+ with ResNet-50)74.6%Conservative-Progressive Collaborative Learning for Semi-supervised Semantic Segmentation-
s4GAN (DeepLab v2 ImageNet pre-trained)59.3%Semi-Supervised Semantic Segmentation with High- and Low-level Consistency-
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Semi Supervised Semantic Segmentation On 2 | SOTA | HyperAI