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⌀ | metric_value
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⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
MDHRM
|
https://arxiv.org/abs/2404.06443v1
|
Average F1
|
66.2
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50)
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
63.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50)
|
https://arxiv.org/abs/2205.01782v2
|
Average AUC
|
92.9
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B)
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
62.4
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B)
|
https://arxiv.org/abs/2205.01782v2
|
Average AUC
|
92.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
JAA-Net
|
http://arxiv.org/abs/1803.05588v2
|
Average F1
|
56.0
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
DRML
|
http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html
|
Average F1
|
26.7
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
DRML
|
http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html
|
Average AUC
|
52.3
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D+
|
FMAE-IAT
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
66.8
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D+
|
Norface
|
https://arxiv.org/abs/2407.15617v1
|
Average F1
|
66.7
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D+
|
FMAE
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
66.2
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
FMAE-IAT
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
67.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
FMAE
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
66.6
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
MDHRD
|
https://arxiv.org/abs/2404.06443v1
|
Average F1
|
66.6
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B)
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
65.5
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B)
|
https://arxiv.org/abs/2205.01782v2
|
Average AUC
|
83.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50)
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
64.7
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50)
|
https://arxiv.org/abs/2205.01782v2
|
Average AUC
|
82.6
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Multi-View Dynamic Facial Action Unit Detection
|
http://arxiv.org/abs/1704.07863v2
|
Average F1
|
63.0
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
Swin-B
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
62.6
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
JAA-Net
|
http://arxiv.org/abs/1803.05588v2
|
Average F1
|
60.0
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
ResNet 50
|
https://arxiv.org/abs/2205.01782v2
|
Average F1
|
59.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
DRML
|
http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html
|
Average F1
|
48.3
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
BP4D
|
DRML
|
http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html
|
Average AUC
|
56.0
|
Facial Recognition and Modelling > Gender Prediction
|
AgeDB
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy
|
98.3
|
Facial Recognition and Modelling > Gender Prediction
|
LAGENDA
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Accuracy
|
97.99
|
Facial Recognition and Modelling > Gender Prediction
|
LAGENDA
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy
|
97.36
|
Facial Recognition and Modelling > Gender Prediction
|
FotW Gender
|
PAENet
|
https://dl.acm.org/doi/10.1145/3323873.3325053
|
Accuracy (%)
|
92.93
|
Facial Recognition and Modelling > Gender Prediction
|
FotW Gender
|
SIAT MMLAB
|
https://ieeexplore.ieee.org/document/7789583
|
Accuracy (%)
|
92.69
|
Facial Recognition and Modelling > Facial Attribute Classification
|
DiveFace
|
Neighbour Learning
|
https://arxiv.org/abs/2208.08382v1
|
Accuracy (%)
|
98.60
|
Facial Recognition and Modelling > Facial Attribute Classification
|
UTKFace
|
Neighbour Learning
|
https://arxiv.org/abs/2208.08382v1
|
Accuracy (%)
|
94.76
|
Facial Recognition and Modelling > Facial Attribute Classification
|
CelebV-HQ
|
MARLIN
|
https://arxiv.org/abs/2211.06627v3
|
Accuracy
|
93.9
|
Facial Recognition and Modelling > Facial Attribute Classification
|
CelebV-HQ
|
MARLIN
|
https://arxiv.org/abs/2211.06627v3
|
AUC
|
0.9561
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
gender-top1
|
97.5
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
age-top1
|
62.28
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
gender-top1
|
95.73
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
age-top1
|
61.07
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
FairFace
|
https://arxiv.org/abs/1908.04913v1
|
race-top1
|
93.7
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
FairFace
|
https://arxiv.org/abs/1908.04913v1
|
gender-top1
|
94.2
|
Facial Recognition and Modelling > Facial Attribute Classification
|
FairFace
|
FairFace
|
https://arxiv.org/abs/1908.04913v1
|
age-top1
|
59.7
|
Facial Recognition and Modelling > Facial Attribute Classification
|
MORPH
|
Neighbour Learning
|
https://arxiv.org/abs/2208.08382v1
|
Accuracy (%)
|
96.41
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
Label2Label
|
https://arxiv.org/abs/2207.08677v1
|
Error Rate
|
12.49
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
SSP + SSG
|
http://arxiv.org/abs/1704.08740v1
|
Error Rate
|
12.87
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
SSPL
|
http://openaccess.thecvf.com//content/CVPR2021/html/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.html
|
Error Rate
|
13.47
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
MCNN-AUX
|
http://arxiv.org/abs/1604.07360v1
|
Error Rate
|
13.69
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
DMTL
|
http://arxiv.org/abs/1706.00906v3
|
Error Rate
|
13.85
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
LNets+ANet
|
https://arxiv.org/abs/1411.7766v3
|
Error Rate
|
16.15
|
Facial Recognition and Modelling > Facial Attribute Classification
|
LFWA
|
PANDA
|
http://arxiv.org/abs/1311.5591v2
|
Error Rate
|
18.97
|
Facial Recognition and Modelling > Facial Attribute Classification
|
bFFHQ
|
DebiAN
|
https://arxiv.org/abs/2207.10077v2
|
Bias-Conflicting Accuracy
|
62.8
|
Facial Recognition and Modelling > Facial Attribute Classification
|
bFFHQ
|
DCWP
|
https://arxiv.org/abs/2210.05247v3
|
Bias-Conflicting Accuracy
|
60.35
|
Facial Recognition and Modelling > Facial Attribute Classification
|
bFFHQ
|
BiaSwap
|
https://arxiv.org/abs/2108.10008v1
|
Bias-Conflicting Accuracy
|
58.87
|
Facial Recognition and Modelling > Action Unit Detection
|
BP4D
|
AU R-CNN
|
https://arxiv.org/abs/1812.05788v2
|
Avg F1
|
63.1
|
Facial Recognition and Modelling > Age And Gender Classification
|
BN-AuthProf
|
Multinomial Naive Bayes (MNB)
|
https://arxiv.org/abs/2412.02058v1
|
F1 score
|
0.905
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Accuracy (5-fold)
|
97.39
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
ViT-hSeq
|
https://arxiv.org/abs/2403.12483v2
|
Accuracy (5-fold)
|
96.56
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy (5-fold)
|
96.51
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
RetinaFace + ArcFace + MLP + Skip connections
|
https://arxiv.org/abs/2108.08186v2
|
Accuracy (5-fold)
|
90.66
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
CPG (single crop, pytorch)
|
https://arxiv.org/abs/1910.06562v3
|
Accuracy (5-fold)
|
89.66
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
PAENet (single crop, tensorflow)
|
https://dl.acm.org/doi/10.1145/3323873.3325053
|
Accuracy (5-fold)
|
89.08
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN ( over-sample, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
86.8
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN (single crop, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
85.9
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
LMTCNN-2-1 (single crop, tensorflow)
|
http://arxiv.org/abs/1806.02023v1
|
Accuracy (5-fold)
|
85.16
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Gender
|
Levi_Hassner CNN (single crop, tensorflow)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
82.52
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
ViT-hSeq
|
https://arxiv.org/abs/2403.12483v2
|
Accuracy (5-fold)
|
84.91
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
MiVOLO-V2
|
https://arxiv.org/abs/2403.02302v4
|
Accuracy (5-fold)
|
69.43
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
MiVOLO-D1
|
https://arxiv.org/abs/2307.04616v2
|
Accuracy (5-fold)
|
68.69
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
AL-ResNets-34 + IMDB-WIKI
|
https://arxiv.org/abs/1805.10445v2
|
Accuracy (5-fold)
|
67.47
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
R-SAAFc2 +IMDB-WIKI
|
http://proceedings.mlr.press/v54/hou17a.html
|
Accuracy (5-fold)
|
67.3
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
RoR-34 + IMDB-WIKI
|
http://arxiv.org/abs/1710.02985v1
|
Accuracy (5-fold)
|
66.74
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
MWR
|
https://arxiv.org/abs/2203.13122v1
|
Accuracy (5-fold)
|
62.6
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
UNIORD-ResNet-101 (single crop, pytorch)
|
https://arxiv.org/abs/2011.07607v2
|
Accuracy (5-fold)
|
61
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
RetinaFace + ArcFace + MLP + IC + Skip connections
|
https://arxiv.org/abs/2108.08186v2
|
Accuracy (5-fold)
|
60.86
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
CPG (single crop, pytorch)
|
https://arxiv.org/abs/1910.06562v3
|
Accuracy (5-fold)
|
57.66
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
PAENet (single crop, tensorflow)
|
https://dl.acm.org/doi/10.1145/3323873.3325053
|
Accuracy (5-fold)
|
57.3
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
MegaAge
|
http://arxiv.org/abs/1708.09687v2
|
Accuracy (5-fold)
|
56.01
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (over-sample, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
50.7
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (single crop, caffe)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
49.5
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
LMTCNN-2-1 (single crop, tensorflow)
|
http://arxiv.org/abs/1806.02023v1
|
Accuracy (5-fold)
|
44.26
|
Facial Recognition and Modelling > Age And Gender Classification
|
Adience Age
|
Levi_Hassner CNN (single crop, tensorflow)
|
https://talhassner.github.io/home/publication/2015_CVPR
|
Accuracy (5-fold)
|
44.14
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
HiFaceGAN
|
https://arxiv.org/abs/2005.05005v2
|
FID
|
11.389
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
HiFaceGAN
|
https://arxiv.org/abs/2005.05005v2
|
LPIPS
|
0.2449
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
HiFaceGAN
|
https://arxiv.org/abs/2005.05005v2
|
NIQE
|
6.767
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
ESRGAN
|
http://arxiv.org/abs/1809.00219v2
|
FID
|
50.901
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
ESRGAN
|
http://arxiv.org/abs/1809.00219v2
|
LPIPS
|
0.3928
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
ESRGAN
|
http://arxiv.org/abs/1809.00219v2
|
NIQE
|
15.383
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
WaveletCNN
|
http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html
|
FID
|
60.916
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
WaveletCNN
|
http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html
|
LPIPS
|
0.4909
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
WaveletCNN
|
http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html
|
NIQE
|
11.450
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
Super-FAN
|
http://arxiv.org/abs/1712.02765v2
|
FID
|
63.693
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
Super-FAN
|
http://arxiv.org/abs/1712.02765v2
|
LPIPS
|
0.4411
|
Facial Recognition and Modelling > Face Hallucination
|
FFHQ 512 x 512 - 16x upscaling
|
Super-FAN
|
http://arxiv.org/abs/1712.02765v2
|
NIQE
|
7.444
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
SCA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
FSIM
|
72.9%
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
SCA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
FID
|
18.2
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
SCA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
NLDA
|
78
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
CA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
FSIM
|
72.7%
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
CA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
FID
|
19.6
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
CA-GAN
|
https://arxiv.org/abs/1712.00899v4
|
NLDA
|
78.1
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
Residual net + Pseudo Sketch Feature Loss + LSGAN
|
https://arxiv.org/abs/1812.04929v2
|
FSIM
|
71.59%
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
CUFSF
|
Residual net + Pseudo Sketch Feature Loss + LSGAN
|
https://arxiv.org/abs/1812.04929v2
|
SSIM
|
40.85%
|
Facial Recognition and Modelling > Face Sketch Synthesis
|
SKSF-A
|
StyleSketch
|
https://arxiv.org/abs/2403.11263v1
|
LPIPS
|
0.1772
|
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