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ResearchFormerly WizWand

Facial Expression Recognition on SFEW 2.0 (val)

61.92AccuracyAPVIT
36.743.247549.79556.3425Oct 7, 2016Oct 18, 2017Oct 29, 2018Nov 9, 2019Nov 19, 2020Nov 30, 2021Dec 11, 2022
Updated 8mo ago

Evaluation results

MethodLinks
APVITPre-training=RAF-DB2022.1261.92
MA-NetPre-training=FER20132022.1259.4
AHA2022.1258.89
Model-4Covariance Pooling=true, SPD Layers=Cov-BiRe-LogEig, Architecture=FC2000-FC512-FC72018.0558.14
DAN2022.1257.88
APMPre-training=RAF-DB2022.1257.57
PAT-Res101 + attrBackbone=ResNet-101, Features=Attributes included2022.1257.57
Model-3Covariance Pooling=true, SPD Layers=Cov-BiRe-BiRe-LogEig2018.0557.48
Model-2Covariance Pooling=true, SPD Layers=Cov-BiRe-LogEig, Architecture=FC2000-FC128-FC72018.0556.72
RANBackbone=VGG16+ResNet182022.1256.4
CMU††Pretraining=FER combined, ensemble=true2016.1055.96
EmotiW-1year=20152018.0555.96
Model-1Covariance Pooling=true, SPD Layers=Cov-BiRe-LogEig2018.0555.4
LBAN-IL2022.1255.28
VGG-VD-16Pretraining=VGGFaces+FER2016.1054.82
VGG-VD-162018.0554.82
Baseline ModelCovariance Pooling=false2018.0554.45
PAT-Res101Backbone=ResNet-1012022.1253.9
HDC††Pretraining=FER + TFD, ensemble=true2016.1052.8
EmotiW-2year=20152018.0552.8
HDC*Pretraining=FER + TFD, best_single_CNN=true2016.1052.5
Kim's CNN2022.1252.5
CMU*Pretraining=FER combined, best_single_CNN=true2016.1052.29
DLP-CNN2022.1251.05
Resnet-50Pretraining=VGGFaces+FER2016.1050.91
Resnet-50Pretraining=VGGFaces2016.1047.48
VGG-MPretraining=VGGFaces+FER2016.1047.02
VGG-VD-16Pretraining=VGGFaces2016.1043.58
VGG-MPretraining=VGGFaces2016.1042.9
AlexNetPretraining=VGGFaces+FER2016.1038.07
AlexNetPretraining=VGGFaces2016.1037.67