task_path
stringlengths 3
199
⌀ | dataset
stringlengths 1
128
⌀ | model_name
stringlengths 1
223
⌀ | paper_url
stringlengths 21
601
⌀ | metric_name
stringlengths 1
50
⌀ | metric_value
stringlengths 1
9.22k
⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Face Recognition
|
IJB-B
|
ArcFace + MS1MV2 + R100
|
https://arxiv.org/abs/2204.00964v2
|
Rank-1
|
0.9450
|
Facial Recognition and Modelling > Face Recognition
|
IJB-B
|
ElasticFace-Cos
|
https://arxiv.org/abs/2109.09416v4
|
TAR @ FAR=0.0001
|
0.953
|
Facial Recognition and Modelling > Face Recognition
|
IJB-B
|
AdaFace + MS1MV3 + R100
|
https://arxiv.org/abs/2204.00964v2
|
TAR @ FAR=0.0001
|
0.9425
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
MFR-ALL
|
97.85
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
MFR-MASK
|
90.88
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
African
|
98.07
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
Caucasian
|
98.81
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
South Asian
|
98.66
|
Facial Recognition and Modelling > Face Recognition
|
MFR
|
Partial FC
|
https://arxiv.org/abs/2203.15565v1
|
East Asian
|
89.97
|
Facial Recognition and Modelling > Face Recognition
|
AgeDB-30
|
Prodpoly
|
https://arxiv.org/abs/2006.13026v2
|
Accuracy
|
0.98467
|
Facial Recognition and Modelling > Face Recognition
|
AgeDB-30
|
ElasticFace-Cos
|
https://arxiv.org/abs/2109.09416v4
|
Accuracy
|
0.9835
|
Facial Recognition and Modelling > Face Recognition
|
AgeDB-30
|
Transformer loss+ArcFace ResNet100
|
https://arxiv.org/abs/2412.02198v2
|
Accuracy
|
0.9831
|
Facial Recognition and Modelling > Face Recognition
|
AgeDB-30
|
DCQ
|
https://arxiv.org/abs/2105.11113v1
|
Accuracy
|
0.9823
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
GhostFaceNetV2-1 (MS1MV3)
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
0.998667
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
SymFace + AdaFace + ResNet100 +WebFace (MS1MV2)
|
https://arxiv.org/abs/2409.11816v1
|
Accuracy
|
0.9985
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
Prodpoly
|
https://arxiv.org/abs/2006.13026v2
|
Accuracy
|
0.99833
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
DiscFace
|
https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html
|
Accuracy
|
0.9983
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
ArcFace + MS1MV2 + R100
|
https://arxiv.org/abs/2204.00964v2
|
Accuracy
|
0.9983
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
DCQ
|
https://arxiv.org/abs/2105.11113v1
|
Accuracy
|
0.998
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
AdaFace + WebFace4M + R100
|
https://arxiv.org/abs/2204.00964v2
|
Accuracy
|
0.9980
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
EdgeFace - S (g=0.5)
|
https://arxiv.org/abs/2307.01838v2
|
Accuracy
|
0.9978
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
CircleLoss
|
https://arxiv.org/abs/2002.10857v2
|
Accuracy
|
0.9973
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
FaceTransformer+OctupletLoss
|
https://arxiv.org/abs/2207.06726v2
|
Accuracy
|
0.9973
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
EdgeFace - XS (g=0.6)
|
https://arxiv.org/abs/2307.01838v2
|
Accuracy
|
0.9973
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
Accuracy
|
0.9850
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
OcularAI-Face
|
https://arxiv.org/abs/2303.13863v1
|
Accuracy
|
0.945
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
OcularAI-Face
|
https://arxiv.org/abs/2303.13863v1
|
F1-score
|
0.9421
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
OcularAI-Face
|
https://arxiv.org/abs/2303.13863v1
|
Recall
|
0.896
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
OcularAI-Face
|
https://arxiv.org/abs/2303.13863v1
|
Precision
|
0.9934
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
PIC - MagFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
0.05
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
PIC - QMagFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
0.05
|
Facial Recognition and Modelling > Face Recognition
|
LFW
|
PIC - ArcFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
4.38
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
Fine-tuned ArcFace
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
95.43
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
Fine-tuned FaceNet
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
93.58
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
ArcFace
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
91.78
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
Fine-tuned VGG-Face
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
91.51
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
90.96
|
Facial Recognition and Modelling > Face Recognition
|
CelebA+masks
|
VGG-Face
|
https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/
|
Accuracy
|
84.56
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET
|
PIC - QMagFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
3.24
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET
|
PIC - MagFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
3.92
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET
|
PIC - ArcFace
|
https://arxiv.org/abs/2211.12483v3
|
FNMR [%] @ 10-3 FMR
|
4.22
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET
|
VGG based
|
https://arxiv.org/abs/2401.01227v2
|
5-class test accuracy
|
99.2%
|
Facial Recognition and Modelling > Face Recognition
|
Carl
|
Model with Up Convolution + DoG Filter (Aligned)
|
https://arxiv.org/abs/2002.04219v1
|
Rank-1
|
85
|
Facial Recognition and Modelling > Face Recognition
|
Carl
|
DPM
|
http://arxiv.org/abs/1601.05347v2
|
Rank-1
|
71
|
Facial Recognition and Modelling > Face Recognition
|
UHDB31
|
Wang et al. [5]
|
https://arxiv.org/abs/1911.07538v2
|
Rank-1
|
94.5
|
Facial Recognition and Modelling > Face Recognition
|
UHDB31
|
Multi-task
|
https://arxiv.org/abs/2011.12427v2
|
Rank-1
|
84.32
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
0.9933
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
SymFace + AdaFace + ResNet100 +WebFace
|
https://arxiv.org/abs/2409.11816v1
|
Accuracy
|
0.992
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
ElasticFace-Arc
|
https://arxiv.org/abs/2109.09416v4
|
Accuracy
|
0.9867
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
DiscFace
|
https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html
|
Accuracy
|
0.9854
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
CircleLoss(ours)
|
https://arxiv.org/abs/2002.10857v2
|
Accuracy
|
0.9602
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
EdgeFace - S (g=0.5)
|
https://arxiv.org/abs/2307.01838v2
|
Accuracy
|
0.9581
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
DCQ
|
https://arxiv.org/abs/2105.11113v1
|
Accuracy
|
0.9287
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FP
|
QMagFace
|
https://arxiv.org/abs/2111.13475v3
|
Accuracy
|
0.8395
|
Facial Recognition and Modelling > Face Recognition
|
MFW+ (M-M)
|
ArcFace + PPL
|
https://bmvc2022.mpi-inf.mpg.de/723/
|
TAR@FAR=0.0001
|
78.80
|
Facial Recognition and Modelling > Face Recognition
|
MFW+ (M-M)
|
MaskInv-HG
|
https://bmvc2022.mpi-inf.mpg.de/723/
|
TAR@FAR=0.0001
|
78.36
|
Facial Recognition and Modelling > Face Recognition
|
MFW+ (M-M)
|
FocusFace
|
https://bmvc2022.mpi-inf.mpg.de/723/
|
TAR@FAR=0.0001
|
77.77
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET (Online Open Set)
|
FaceNet+Adaptive Threshold
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
83.79
|
Facial Recognition and Modelling > Face Recognition
|
Color FERET (Online Open Set)
|
FaceNet+Fixed Threshold (0.3968)
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
80.72
|
Facial Recognition and Modelling > Face Recognition
|
EURECOM
|
Model with Up Convolution + DoG Filter
|
https://arxiv.org/abs/2002.04219v1
|
Rank-1
|
88.33
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
Fine-tuned ArcFace
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
91.47
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
Fine-tuned FaceNet
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
88.06
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
ArcFace
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
87.95
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
Fine-tuned VGG-Face
|
https://arxiv.org/abs/2109.01745v5
|
Accuracy
|
86.85
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
84.21
|
Facial Recognition and Modelling > Face Recognition
|
CASIA-WebFace+masks
|
VGG-Face
|
https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/
|
Accuracy
|
79.65
|
Facial Recognition and Modelling > Face Recognition
|
LFW (Online Open Set)
|
FaceNet+Adaptive Threshold
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
76.46
|
Facial Recognition and Modelling > Face Recognition
|
LFW (Online Open Set)
|
FaceNet+Fixed Threshold (0.3779)
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
53.97
|
Facial Recognition and Modelling > Face Recognition
|
Adience (Online Open Set)
|
FaceNet+Adaptive Threshold
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
84.3
|
Facial Recognition and Modelling > Face Recognition
|
Adience (Online Open Set)
|
FaceNet+Fixed Threshold (0.2487)
|
http://arxiv.org/abs/1810.11160v1
|
Average Accuracy (10 times)
|
80.6
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
MS1MV2, R100, SFace
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
91.57
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
MS1MV2, R100, Arcface
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
90.57
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
MS1MV2, R100, Curricularface
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
90.43
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
VGGFace2, R50, ArcFace
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
85.95
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
CASIA-WebFace, R50, CosFace
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
82.52
|
Facial Recognition and Modelling > Face Recognition
|
MLFW
|
Private-Asia, R50, ArcFace
|
https://arxiv.org/abs/2109.05804v2
|
Accuracy
|
77.20
|
Facial Recognition and Modelling > Face Recognition
|
CPLFW
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
0.9465
|
Facial Recognition and Modelling > Face Recognition
|
CPLFW
|
ElasticFace-Arc
|
https://arxiv.org/abs/2109.09416v4
|
Accuracy
|
0.9327
|
Facial Recognition and Modelling > Face Recognition
|
CALFW
|
Prodpoly
|
https://arxiv.org/abs/2006.13026v2
|
Accuracy
|
0.96233
|
Facial Recognition and Modelling > Face Recognition
|
CALFW
|
ElasticFace-Arc
|
https://arxiv.org/abs/2109.09416v4
|
Accuracy
|
0.9617
|
Facial Recognition and Modelling > Face Recognition
|
CALFW
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
0.9612
|
Facial Recognition and Modelling > Face Recognition
|
CFP-FF
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
99.9143
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
Dual-Branch Network
|
https://arxiv.org/abs/2405.08555v1
|
SRCC
|
0.85
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
Dual-Branch Network
|
https://arxiv.org/abs/2405.08555v1
|
PLCC
|
0.86
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
Dual-Branch Network
|
https://arxiv.org/abs/2405.08555v1
|
KRCC
|
0.68
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
Dual-Branch Network
|
https://arxiv.org/abs/2405.08555v1
|
MAE
|
0.53
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
PICNIQ
|
https://arxiv.org/abs/2403.09746v2
|
SRCC
|
0.81
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
PICNIQ
|
https://arxiv.org/abs/2403.09746v2
|
PLCC
|
0.82
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
PICNIQ
|
https://arxiv.org/abs/2403.09746v2
|
KRCC
|
0.62
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
PICNIQ
|
https://arxiv.org/abs/2403.09746v2
|
MAE
|
0.72
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
FHIQA
|
https://arxiv.org/abs/2402.09178v1
|
SRCC
|
0.78
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
FHIQA
|
https://arxiv.org/abs/2402.09178v1
|
PLCC
|
0.78
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
FHIQA
|
https://arxiv.org/abs/2402.09178v1
|
KRCC
|
0.59
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
PIQ23
|
FHIQA
|
https://arxiv.org/abs/2402.09178v1
|
MAE
|
1.12
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
DSL-FIQA
|
https://arxiv.org/abs/2406.09622v1
|
PLCC
|
0.9745
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
DSL-FIQA
|
https://arxiv.org/abs/2406.09622v1
|
SRCC
|
0.9740
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
StyleGAN-IQA
|
https://arxiv.org/abs/2207.04904v2
|
PLCC
|
0.9673
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
StyleGAN-IQA
|
https://arxiv.org/abs/2207.04904v2
|
SRCC
|
0.9684
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
IFQA
|
https://arxiv.org/abs/2211.07077v2
|
PLCC
|
0.9601
|
Facial Recognition and Modelling > Face Recognition > Face Image Quality Assessment
|
CGFIQA-40k
|
IFQA
|
https://arxiv.org/abs/2211.07077v2
|
SRCC
|
0.9603
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.