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⌀ | metric_value
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⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-OR-CNN
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.16
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-Mean-Variance
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.16
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-DLDL-v2
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.15
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-Cross-Entropy
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.14
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-DLDL
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.14
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
ResNet-50-SORD
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.14
|
Facial Recognition and Modelling > Age Estimation
|
AFAD
|
FaRL+MLP
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.12
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
MetaAge
|
https://arxiv.org/abs/2207.05288v1
|
MAE
|
3.49
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
MetaAge
|
https://arxiv.org/abs/2207.05288v1
|
e-error
|
0.2651
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
DLDL-v2 (ThinAgeNet)
|
https://arxiv.org/abs/2007.01771v2
|
MAE
|
3.452
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
DLDL-v2 (ThinAgeNet)
|
https://arxiv.org/abs/2007.01771v2
|
e-error
|
0.267
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
Mean-Variance
|
http://openaccess.thecvf.com/content_cvpr_2018/html/Pan_Mean-Variance_Loss_for_CVPR_2018_paper.html
|
e-error
|
0.2867
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2016
|
FaRL+MLP
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.38
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
MetaAge
|
https://arxiv.org/abs/2207.05288v1
|
MAE
|
2.83
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
MetaAge
|
https://arxiv.org/abs/2207.05288v1
|
e-error
|
0.250651
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
BridgeNet
|
http://arxiv.org/abs/1904.03358v1
|
MAE
|
2.87
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
BridgeNet
|
http://arxiv.org/abs/1904.03358v1
|
e-error
|
0.255140
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
DEX
|
https://link.springer.com/article/10.1007/s11263-016-0940-3
|
e-error
|
0.264975
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
AgeNet
|
https://openaccess.thecvf.com/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf
|
e-error
|
0.270685
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
DLDL-v2 (ThinAgeNet)
|
https://arxiv.org/abs/2007.01771v2
|
MAE
|
3.135
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
DLDL-v2 (ThinAgeNet)
|
https://arxiv.org/abs/2007.01771v2
|
e-error
|
0.272
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
DLDL+VGG-Face
|
http://arxiv.org/abs/1611.01731v2
|
MAE
|
3.51
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
DLDL+VGG-Face
|
http://arxiv.org/abs/1611.01731v2
|
e-error
|
0.31
|
Facial Recognition and Modelling > Age Estimation
|
ChaLearn 2015
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
MAE
|
2.95
|
Facial Recognition and Modelling > Age Estimation
|
IMDB-Clean
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Average mean absolute error
|
3.97
|
Facial Recognition and Modelling > Age Estimation
|
IMDB-Clean
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Average mean absolute error
|
4.09
|
Facial Recognition and Modelling > Age Estimation
|
IMDB-Clean
|
VOLO-D1 age&gender
|
https://arxiv.org/abs/2307.04616v2
|
Average mean absolute error
|
4.22
|
Facial Recognition and Modelling > Age Estimation
|
IMDB-Clean
|
FP-Age
|
https://arxiv.org/abs/2106.11145v2
|
Average mean absolute error
|
4.68
|
Facial Recognition and Modelling > Age Estimation
|
PhysioNet Challenge 2021
|
Inception Time
|
https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3
|
Mean absolute error
|
8.3
|
Facial Recognition and Modelling > Age Estimation
|
PhysioNet Challenge 2021
|
Inception Time
|
https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3
|
Mean Squared Error
|
111
|
Facial Recognition and Modelling > Age Estimation
|
PhysioNet Challenge 2021
|
Inception Time
|
https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3
|
Mean Squared Error (cross-val)
|
117.5±2.7
|
Facial Recognition and Modelling > Age Estimation
|
PhysioNet Challenge 2021
|
Inception Time
|
https://www.medrxiv.org/content/10.1101/2022.10.03.22280640v3
|
Mean Absolute Error (cross-val)
|
7.90±0.04
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
OrdinalCLIP
|
https://arxiv.org/abs/2206.02338v2
|
MAE
|
0.47
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
OrdinalCLIP
|
https://arxiv.org/abs/2206.02338v2
|
Accuracy
|
61.2
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
POE
|
https://arxiv.org/abs/2103.13629v1
|
MAE
|
0.47
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
POE
|
https://arxiv.org/abs/2103.13629v1
|
Accuracy
|
60.5
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
SORD
|
http://openaccess.thecvf.com/content_CVPR_2019/html/Diaz_Soft_Labels_for_Ordinal_Regression_CVPR_2019_paper.html
|
MAE
|
0.49
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
SORD
|
http://openaccess.thecvf.com/content_CVPR_2019/html/Diaz_Soft_Labels_for_Ordinal_Regression_CVPR_2019_paper.html
|
Accuracy
|
59.6
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
GP-DNNOR
|
http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Probabilistic_Deep_Ordinal_Regression_Based_on_Gaussian_Processes_ICCV_2019_paper.html
|
MAE
|
0.54
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
GP-DNNOR
|
http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Probabilistic_Deep_Ordinal_Regression_Based_on_Gaussian_Processes_ICCV_2019_paper.html
|
Accuracy
|
57.4
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
CNNPOR
|
http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html
|
MAE
|
0.55
|
Facial Recognition and Modelling > Age Estimation
|
mebeblurf
|
CNNPOR
|
http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_A_Constrained_Deep_CVPR_2018_paper.html
|
Accuracy
|
57.4
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
MAE
|
3.7
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
FaRL+MLP
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.87
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
VOLO-D1 age&gender
|
https://arxiv.org/abs/2307.04616v2
|
MAE
|
4.23
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-SORD
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.36
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
MAE
|
4.37
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-Cross-Entropy
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.38
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-DLDL
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.39
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-OR-CNN
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.40
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-DLDL-v2
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.42
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-Mean-Variance
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.42
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-Unimodal-Concentrated
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.47
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
Randomized Bins
|
https://arxiv.org/abs/2006.15864v1
|
MAE
|
4.55
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
ResNet-50-Regression
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.72
|
Facial Recognition and Modelling > Age Estimation
|
UTKFace
|
CORAL
|
https://arxiv.org/abs/1901.07884v7
|
MAE
|
5.39
|
Facial Recognition and Modelling > Age Estimation
|
KANFace
|
FP-Age
|
https://arxiv.org/abs/2106.11145v2
|
Average mean absolute error
|
6.81
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
MAE
|
2.23
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
BridgeNet
|
http://arxiv.org/abs/1904.03358v1
|
MAE
|
2.56
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
C3AE (WIKI-IMDB)
|
http://arxiv.org/abs/1904.05059v2
|
MAE
|
2.95
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
DEX
|
https://link.springer.com/article/10.1007/s11263-016-0940-3
|
MAE
|
3.09
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
CMAAE-OR
|
http://arxiv.org/abs/1804.02740v1
|
MAE
|
3.62
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
DRFs
|
http://arxiv.org/abs/1712.07195v1
|
MAE
|
3.85
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
Zhu et al. (Actual)
|
http://arxiv.org/abs/1804.02740v1
|
MAE
|
4.58
|
Facial Recognition and Modelling > Age Estimation
|
FGNET
|
AEBFI
|
http://arxiv.org/abs/1904.05059v2
|
MAE
|
52
|
Facial Recognition and Modelling > Age Estimation
|
MORPH
|
CMAAE-OR
|
http://arxiv.org/abs/1804.02740v1
|
MAE
|
1.48
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
MAE
|
3.89
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-Cross-Entropy
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.96
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-DLDL
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.96
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-DLDL-v2
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.96
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-SORD
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.96
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
FaRL+MLP
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.96
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-OR-CNN
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.01
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-Regression
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.06
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-Mean-Variance
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.07
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
ResNet-50-Unimodal-Concentrated
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
4.10
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
MAE
|
4.41
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
RNDF
|
https://arxiv.org/abs/1908.10737v1
|
MAE
|
4.60
|
Facial Recognition and Modelling > Age Estimation
|
CACD
|
CORAL
|
https://arxiv.org/abs/1901.07884v7
|
MAE
|
5.35
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
Hierarchical Attention-based Age Estimation (RS)
|
https://arxiv.org/abs/2103.09882v2
|
MAE
|
1.13
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
MataAge
|
https://arxiv.org/abs/2207.05288v1
|
MAE
|
1.81
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
DLDL-v2 (ThinAgeNet)
|
https://arxiv.org/abs/2007.01771v2
|
MAE
|
1.969
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
MAE
|
2.00
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
CS
|
95.0
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
DLDL+VGG-Face
|
http://arxiv.org/abs/1611.01731v2
|
MAE
|
2.42±0.01
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
DLDL+VGG-Face (KL, Max)3
|
http://arxiv.org/abs/1611.01731v2
|
MAE
|
2.42
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
MegaAge (w. IMDB-WIKI)
|
http://arxiv.org/abs/1708.09687v2
|
MAE
|
2.52
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
Hierarchical Attention-based Age Estimation (SE)
|
https://arxiv.org/abs/2103.09882v2
|
MAE
|
2.53
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2
|
CORAL
|
https://arxiv.org/abs/1901.07884v7
|
MAE
|
2.59
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-Unimodal-Concentrated
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.78
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-Cross-Entropy
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.81
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-DLDL
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.81
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-SORD
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.81
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-DLDL-v2
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.82
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-OR-CNN
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.83
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-Mean-Variance
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.83
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
ResNet-50-Regression
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
2.83
|
Facial Recognition and Modelling > Age Estimation
|
MORPH Album2 (SE)
|
FaRL+MLP
|
https://arxiv.org/abs/2307.04570v3
|
MAE
|
3.04
|
Facial Recognition and Modelling > Age Estimation > Few-shot Age Estimation
|
MORPH Album2
|
OrdinalCLIP
|
https://arxiv.org/abs/2206.02338v2
|
MAE
|
4.94
|
Facial Recognition and Modelling > Age Estimation > Few-shot Age Estimation
|
MORPH Album2
|
OrdinalCLIP
|
https://arxiv.org/abs/2206.02338v2
|
MAE (2 shot)
|
4.36
|
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