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⌀ | dataset
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⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
FiFA
|
https://arxiv.org/abs/2402.15044v1
|
Mean NME
|
0.92
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
FiFA
|
https://arxiv.org/abs/2402.15044v1
|
NME
|
0.92
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
AnchorFace
|
https://arxiv.org/abs/2007.03221v3
|
Mean NME
|
1.56
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
AnchorFace
|
https://arxiv.org/abs/2007.03221v3
|
Mean NME
|
1.56
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
SAN
|
http://arxiv.org/abs/1803.04108v4
|
Mean NME
|
1.91
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
DCFE (Box height Norm, 19 landmarks - no earlobs)
|
http://openaccess.thecvf.com/content_ECCV_2018/html/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.html
|
Mean NME
|
2.17
|
Facial Recognition and Modelling > Facial Landmark Detection
|
AFLW-Full
|
3DDE (Box height Norm, 19 landmarks - no earlobs)
|
https://arxiv.org/abs/1902.01831v2
|
Mean NME
|
2.01
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
300W
|
DVE
|
https://arxiv.org/abs/1908.06427v1
|
NME
|
4.65
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
300W
|
FAb-Net
|
http://arxiv.org/abs/1808.06882v1
|
NME
|
5.71
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
300W
|
FSE
|
http://arxiv.org/abs/1705.02193v2
|
NME
|
7.97
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
300W
|
DEIL
|
http://arxiv.org/abs/1706.02932v2
|
NME
|
8.23
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
Deep Latent Particles
|
https://arxiv.org/abs/2205.15821v2
|
NME
|
2.43
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
Conditional Image Generation
|
http://arxiv.org/abs/1806.07823v2
|
NME
|
2.54
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
DVE
|
https://arxiv.org/abs/1908.06427v1
|
NME
|
2.86
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
LMDIS-REP
|
http://arxiv.org/abs/1804.04412v1
|
NME
|
3.15
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
Lorenz2019unsupervised
|
https://arxiv.org/abs/1903.06946v3
|
NME
|
3.24
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
FAB-Net
|
http://arxiv.org/abs/1808.06882v1
|
NME
|
3.44
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
AutoLink
|
https://arxiv.org/abs/2205.10636v6
|
NME
|
3.54
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
DEIL
|
http://arxiv.org/abs/1706.02932v2
|
NME
|
4.02
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
Deforming Autoencoders
|
http://arxiv.org/abs/1806.06503v1
|
NME
|
5.45
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
LatentKeypointGAN
|
https://arxiv.org/abs/2103.15812v5
|
NME
|
5.85
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
Thewlis2017unsupervised
|
http://arxiv.org/abs/1705.02193v2
|
NME
|
6.32
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
FSE
|
http://arxiv.org/abs/1705.02193v2
|
NME
|
6.67
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL
|
TCDCN
|
https://arxiv.org/abs/1408.3967v4
|
NME
|
7.95
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW-MTFL
|
DVE
|
https://arxiv.org/abs/1908.06427v1
|
NME
|
7.53
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW-MTFL
|
FSE
|
http://arxiv.org/abs/1705.02193v2
|
NME
|
10.53
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW-MTFL
|
DEIL
|
http://arxiv.org/abs/1706.02932v2
|
NME
|
10.99
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
AutoLink
|
https://arxiv.org/abs/2205.10636v6
|
NME
|
5.24
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
GANSeg
|
https://arxiv.org/abs/2112.01036v3
|
NME
|
6.18
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
IMM
|
http://arxiv.org/abs/1806.07823v2
|
NME
|
8.74
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
Lorenz2019unsupervised
|
https://arxiv.org/abs/1903.06946v3
|
NME
|
11.41
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
UPSDAS
|
https://arxiv.org/abs/2105.12405v1
|
NME
|
12.26
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
SCOPS
|
https://arxiv.org/abs/1905.01298v1
|
NME
|
15.01
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
DFF
|
http://arxiv.org/abs/1806.10206v5
|
NME
|
31.30
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
ULD
|
http://arxiv.org/abs/1705.02193v2
|
NME
|
31.3
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
MAFL Unaligned
|
LMDIS-REP
|
http://arxiv.org/abs/1804.04412v1
|
NME
|
40.82
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW Unaligned
|
UPSDAP
|
https://arxiv.org/abs/2105.12405v1
|
NME
|
13.13
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW Unaligned
|
IMM
|
https://arxiv.org/abs/2105.12405v1
|
NME
|
13.31
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW Unaligned
|
Lorenz2019unsupervised
|
https://arxiv.org/abs/2105.12405v1
|
NME
|
13.6
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW Unaligned
|
SCOPS
|
https://arxiv.org/abs/2105.12405v1
|
NME
|
16.05
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW (Zhang CVPR 2018 crops)
|
Conditional Image Generation
|
http://arxiv.org/abs/1806.07823v2
|
NME
|
6.31
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW (Zhang CVPR 2018 crops)
|
DVE
|
https://arxiv.org/abs/1908.06427v1
|
NME
|
6.54
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW (Zhang CVPR 2018 crops)
|
LMDIS-REP
|
http://arxiv.org/abs/1804.04412v1
|
NME
|
6.58
|
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection
|
AFLW (Zhang CVPR 2018 crops)
|
DEIL
|
http://arxiv.org/abs/1706.02932v2
|
NME
|
10.14
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
3D-LFM
|
https://arxiv.org/abs/2312.11894v2
|
Average MPJPE (mm)
|
10.44
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
Large SimpleBaseline
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
14.6
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
SemGAN
|
https://arxiv.org/abs/2406.01196v1
|
Average MPJPE (mm)
|
15.95
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
Jointformer
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
17.8
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
CanonPose + 3D supervision
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
17.9
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
Large SimpleBaseline
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
19.8
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
Jointformer
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
19.8
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
CPN + Jointformer
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
20.7
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
CanonPose + 3D supervision
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
22.2
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
CanonPose
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
24.6
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
SimpleBaseline
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
24.6
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
Resnet50
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
26.3
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
CanonPose
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
31.9
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
SHN + SimpleBaseline
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
32.5
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
H3WB
|
SimpleBaseline
|
https://arxiv.org/abs/2211.15692v2
|
Average MPJPE (mm)
|
34.0
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
AFLW2000-3D
|
JVCR
|
http://arxiv.org/abs/1801.09242v1
|
GTE
|
7.28
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
Urban Hyperspectral Image
|
Lucky Brand 13
|
https://aclanthology.org/2020.coling-main.519
|
10°5 cm
|
13.69
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
3DFAW
|
JVCR
|
http://arxiv.org/abs/1801.09242v1
|
CVGTCE
|
3.46
|
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization
|
3DFAW
|
JVCR
|
http://arxiv.org/abs/1801.09242v1
|
GTE
|
4.35
|
Facial Recognition and Modelling > Face Identification
|
IJB-A
|
StyleFNM
|
https://arxiv.org/abs/2312.14544v1
|
Accuracy
|
94.90%
|
Facial Recognition and Modelling > Face Identification
|
IJB-A
|
Deep Residual Equivariant Mapping
|
http://arxiv.org/abs/1803.00839v1
|
Accuracy
|
94.60%
|
Facial Recognition and Modelling > Face Identification
|
IJB-A
|
FPN
|
http://arxiv.org/abs/1708.07517v2
|
Accuracy
|
91.4%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
Cos+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
Accuracy
|
99.27%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
PartialFC + Glint360K + R100
|
https://arxiv.org/abs/2010.05222v2
|
Accuracy
|
99.10%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
Arc+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
Accuracy
|
98.82%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
Prodpoly
|
https://arxiv.org/abs/2006.13026v2
|
Accuracy
|
98.78%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
GhostFaceNetV2-1
|
https://ieeexplore.ieee.org/document/10098610
|
Accuracy
|
98.64%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
ArcFace + MS1MV2 + R100 + R
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
98.35%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
Mag+UNPG
|
https://arxiv.org/abs/2203.11593v2
|
Accuracy
|
98.03%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
SV-AM-Softmax
|
http://arxiv.org/abs/1812.11317v1
|
Accuracy
|
97.2%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
CosFace
|
http://arxiv.org/abs/1801.09414v2
|
Accuracy
|
82.72%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
SphereFace (3-patch ensemble)
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
75.766%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
Light CNN-29
|
http://arxiv.org/abs/1511.02683v4
|
Accuracy
|
73.749%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
SphereFace (single model)
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
72.729%
|
Facial Recognition and Modelling > Face Identification
|
MegaFace
|
FaceNet
|
http://arxiv.org/abs/1503.03832v3
|
Accuracy
|
70.49%
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
SV-AM-Softmax
|
http://arxiv.org/abs/1812.11317v1
|
Accuracy
|
73.56
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
AM-Softmax
|
http://arxiv.org/abs/1801.05599v4
|
Accuracy
|
61.80
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
Arc-Softmax
|
https://arxiv.org/abs/1801.07698v4
|
Accuracy
|
57.48
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
A-Softmax
|
http://arxiv.org/abs/1704.08063v4
|
Accuracy
|
43.89
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
F-Softmax
|
http://arxiv.org/abs/1708.02002v2
|
Accuracy
|
39.80
|
Facial Recognition and Modelling > Face Identification
|
Trillion Pairs Dataset
|
HM-Softmax
|
http://arxiv.org/abs/1604.03540v1
|
Accuracy
|
36.75
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
CoNAN (Adaface)
|
https://arxiv.org/abs/2307.10237v1
|
Rank1
|
83.33
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
ProxyFusion (Adaface)
|
https://proceedings.neurips.cc/paper_files/paper/2024/hash/81f554467f27759e88de14ba2fbafb47-Abstract-Conference.html
|
Rank1
|
83.33
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
NAN (Adaface)
|
http://arxiv.org/abs/1603.05474v4
|
Rank1
|
80.21
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
MCN (Adaface)
|
http://arxiv.org/abs/1807.09192v1
|
Rank1
|
79.16
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
Naive Averaging (Adaface)
|
http://arxiv.org/abs/1312.4400v3
|
Rank1
|
46.87
|
Facial Recognition and Modelling > Face Identification
|
DroneSURF
|
HOG
|
https://arxiv.org/abs/2204.01712v1
|
Rank1
|
8.33
|
Facial Recognition and Modelling > Face Identification
|
IJB-B
|
FPN
|
http://arxiv.org/abs/1708.07517v2
|
Accuracy
|
91.1%
|
Facial Recognition and Modelling > Facial Inpainting
|
WebFace
|
SymmFCNet (Full)
|
http://arxiv.org/abs/1812.07741v1
|
PSNR
|
27.22
|
Facial Recognition and Modelling > Facial Inpainting
|
FFHQ
|
DMFN
|
https://arxiv.org/abs/2002.02609v2
|
LPIPS
|
0.0457
|
Facial Recognition and Modelling > Facial Inpainting
|
FFHQ
|
DMFN
|
https://arxiv.org/abs/2002.02609v2
|
PSNR
|
26.49
|
Facial Recognition and Modelling > Facial Inpainting
|
FFHQ
|
DMFN
|
https://arxiv.org/abs/2002.02609v2
|
SSIM
|
0.8985
|
Facial Recognition and Modelling > Facial Inpainting
|
VggFace2
|
SymmFCNet (Full)
|
http://arxiv.org/abs/1812.07741v1
|
PSNR
|
27.81
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
Norface
|
https://arxiv.org/abs/2407.15617v1
|
Average F1
|
72.7
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
FMAE_IAT
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
70.1
|
Facial Recognition and Modelling > Facial Action Unit Detection
|
DISFA
|
FMAE
|
https://arxiv.org/abs/2407.11243v2
|
Average F1
|
68.7
|
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