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Semantic Segmentation On Lip Val

Metrics

mIoU

Results

Performance results of various models on this benchmark

Model Name
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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Semantic Segmentation On Lip Val | SOTA | HyperAI