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Datasets/LIP

LIP

Look into Person

ImagesCustom (research, non-research, non-commercial)Introduced 2017-01-01

The LIP (Look into Person) dataset is a large-scale dataset focusing on semantic understanding of a person. It contains 50,000 images with elaborated pixel-wise annotations of 19 semantic human part labels and 2D human poses with 16 key points. The images are collected from real-world scenarios and the subjects appear with challenging poses and view, heavy occlusions, various appearances and low resolution.

Source: http://sysu-hcp.net/lip/ Image Source: http://sysu-hcp.net/lip/

Related Benchmarks

LIP val/10-shot image generation/mIoULIP val/Semantic Segmentation/mIoULiPS/Atomistic Description/MAELiPS/Formation Energy/MAELiPS20/Atomistic Description/MAELiPS20/Formation Energy/MAELip Reading in the Wild/Lipreading/Top-1 AccuracyLip Reading in the Wild/Natural Language Transduction/Top-1 AccuracyLip2Wav (Chem)/Lip to Speech Synthesis/ESTOILip2Wav (Chem)/Lip to Speech Synthesis/PESQLip2Wav (Chem)/Lip to Speech Synthesis/STOILip2Wav (Chem)/Speech Recognition/ESTOILip2Wav (Chem)/Speech Recognition/PESQLip2Wav (Chem)/Speech Recognition/STOILip2Wav (Chem)/Visual Speech Recognition/ESTOILip2Wav (Chem)/Visual Speech Recognition/PESQLip2Wav (Chem)/Visual Speech Recognition/STOILip2Wav (Chess)/Lip to Speech Synthesis/ESTOILip2Wav (Chess)/Lip to Speech Synthesis/PESQLip2Wav (Chess)/Lip to Speech Synthesis/STOILip2Wav (Chess)/Speech Recognition/ESTOILip2Wav (Chess)/Speech Recognition/PESQLip2Wav (Chess)/Speech Recognition/STOILip2Wav (Chess)/Visual Speech Recognition/ESTOILip2Wav (Chess)/Visual Speech Recognition/PESQLip2Wav (Chess)/Visual Speech Recognition/STOILip2Wav (DL)/Lip to Speech Synthesis/ESTOILip2Wav (DL)/Lip to Speech Synthesis/PESQLip2Wav (DL)/Lip to Speech Synthesis/STOILip2Wav (DL)/Speech Recognition/ESTOILip2Wav (DL)/Speech Recognition/PESQLip2Wav (DL)/Speech Recognition/STOILip2Wav (DL)/Visual Speech Recognition/ESTOILip2Wav (DL)/Visual Speech Recognition/PESQLip2Wav (DL)/Visual Speech Recognition/STOILip2Wav (EH)/Lip to Speech Synthesis/ESTOILip2Wav (EH)/Lip to Speech Synthesis/PESQLip2Wav (EH)/Lip to Speech Synthesis/STOILip2Wav (EH)/Speech Recognition/ESTOILip2Wav (EH)/Speech Recognition/PESQLip2Wav (EH)/Speech Recognition/STOILip2Wav (EH)/Visual Speech Recognition/ESTOILip2Wav (EH)/Visual Speech Recognition/PESQLip2Wav (EH)/Visual Speech Recognition/STOILip2Wav (HS)/Lip to Speech Synthesis/ESTOILip2Wav (HS)/Lip to Speech Synthesis/PESQLip2Wav (HS)/Lip to Speech Synthesis/STOILip2Wav (HS)/Speech Recognition/ESTOILip2Wav (HS)/Speech Recognition/PESQLip2Wav (HS)/Speech Recognition/STOILip2Wav (HS)/Visual Speech Recognition/ESTOILip2Wav (HS)/Visual Speech Recognition/PESQLip2Wav (HS)/Visual Speech Recognition/STOILipitK/Domain Adaptation/AccuracyLipitK/Domain Generalization/AccuracyLipogram-e/Conditional Text Generation/Ignored Constraint Error RateLipogram-e/Text Generation/Ignored Constraint Error RateLipophilicity/Atomistic Description/RMSELipophilicity/Graph Regression/RMSELipophilicity/Molecular Property Prediction/RMSELipophilicity /Graph Regression/R2Lipophilicity /Graph Regression/RMSELipophilicity /Graph Regression/RMSE@80%TrainLipophilicity (logd74)/Drug Discovery/RMSE

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Semantic Segmentation