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Semantic Segmentation On Lip Val

Métriques

mIoU

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
mIoU
Paper TitleRepository
MuLA (ResNet-101)49.30%Mutual Learning to Adapt for Joint Human Parsing and Pose Estimation-
HRNetV2 (HRNetV2-W48)55.90%High-Resolution Representations for Labeling Pixels and Regions
HRNetV2 + OCR + RMI (PaddleClas pretrained)58.2%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
Attention+SSL (ResNet-101)44.73%Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Hulk(Finetune, ViT-B)63.98%Hulk: A Universal Knowledge Translator for Human-Centric Tasks
OCR (ResNet-101)55.6%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
JPPNet (ResNet-101)51.37%Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark
UniHCP (finetune)63.86%UniHCP: A Unified Model for Human-Centric Perceptions
MMAN (ResNet-101)46.81%Macro-Micro Adversarial Network for Human Parsing
CE2P (ResNet-101)53.10%Devil in the Details: Towards Accurate Single and Multiple Human Parsing
OCR (HRNetV2-W48)56.65%Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
SOLIDER60.50%Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Hulk(Finetune, ViT-L)66.02%Hulk: A Universal Knowledge Translator for Human-Centric Tasks
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