task_path
stringlengths 3
199
⌀ | dataset
stringlengths 1
128
⌀ | model_name
stringlengths 1
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⌀ | paper_url
stringlengths 21
601
⌀ | metric_name
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⌀ | metric_value
stringlengths 1
9.22k
⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Face Recognition > Unsupervised face recognition
|
AgeDB-30
|
USynthFace
|
https://arxiv.org/abs/2211.07371v1
|
Accuracy
|
71.62
|
Facial Recognition and Modelling > Face Swapping
|
HOD
|
Work
|
https://arxiv.org/abs/2311.11009v1
|
0-shot MRR
|
Good
|
Facial Recognition and Modelling > Face Swapping
|
^(#$!@#$)(()))******
|
Ganesh deka
|
https://arxiv.org/abs/2402.04499v2
|
10%
|
2
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config A)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
98.5
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config A)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
14.97
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config A)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
7.07
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
97.95
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
5.86
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
5.74
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
97.65
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
5.82
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
4.13
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
97.10
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
5.75
|
Facial Recognition and Modelling > Face Swapping
|
AFLW2000-3D
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
4.15
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFaceLab
|
https://arxiv.org/abs/2005.05535v5
|
pose
|
1.12
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFaceLab
|
https://arxiv.org/abs/2005.05535v5
|
SSIM
|
0.73
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFaceLab
|
https://arxiv.org/abs/2005.05535v5
|
perceptual loss
|
0.39
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFaceLab
|
https://arxiv.org/abs/2005.05535v5
|
verification
|
0.61
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFaceLab
|
https://arxiv.org/abs/2005.05535v5
|
landmarks
|
0.73
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
SimSwap-oFM
|
https://arxiv.org/abs/2106.06340v1
|
pose
|
1.22
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
98.84
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
2.04
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config C)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
7.97
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
98.19
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
2.15
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config D)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
5.70
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FIVA
|
https://arxiv.org/abs/2309.04228v1
|
ID retrieval
|
99.25
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FIVA
|
https://arxiv.org/abs/2309.04228v1
|
pose
|
2.16
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
ID retrieval
|
98.54
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
pose
|
2.24
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceDancer (Config B)
|
https://arxiv.org/abs/2210.10473v2
|
exp embedding L2
|
8.52
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
HifiFace
|
https://arxiv.org/abs/2106.09965v1
|
ID retrieval
|
98.48
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
HifiFace
|
https://arxiv.org/abs/2106.09965v1
|
pose
|
2.63
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
MegaFS
|
https://arxiv.org/abs/2105.04932v2
|
ID retrieval
|
90.83
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
MegaFS
|
https://arxiv.org/abs/2105.04932v2
|
pose
|
2.64
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
MegaFS
|
https://arxiv.org/abs/2105.04932v2
|
expression
|
2.96
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
CihaNet
| null |
ID retrieval
|
80.4
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
CihaNet
| null |
pose
|
3.27
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
CihaNet
| null |
SSIM
|
0.992
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFakes
|
https://arxiv.org/abs/2005.05535v5
|
pose
|
4.75
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFakes
|
https://arxiv.org/abs/2005.05535v5
|
SSIM
|
0.71
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFakes
|
https://arxiv.org/abs/2005.05535v5
|
perceptual loss
|
0.41
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFakes
|
https://arxiv.org/abs/2005.05535v5
|
verification
|
0.69
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFakes
|
https://arxiv.org/abs/2005.05535v5
|
landmarks
|
1.15
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
Nirkin et al.
|
https://arxiv.org/abs/2005.05535v5
|
pose
|
6.01
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
Nirkin et al.
|
https://arxiv.org/abs/2005.05535v5
|
SSIM
|
0.65
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
Nirkin et al.
|
https://arxiv.org/abs/2005.05535v5
|
perceptual loss
|
0.5
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
Nirkin et al.
|
https://arxiv.org/abs/2005.05535v5
|
verification
|
0.66
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
Nirkin et al.
|
https://arxiv.org/abs/2005.05535v5
|
landmarks
|
0.35
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
SimSwap-nFM
|
https://arxiv.org/abs/2106.06340v1
|
ID retrieval
|
96.57
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceController
|
https://arxiv.org/abs/2102.11464v1
|
FID
|
3.51
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceSwap
|
https://arxiv.org/abs/2102.11464v1
|
FID
|
3.81
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FaceShifter
|
https://arxiv.org/abs/2102.11464v1
|
FID
|
4.05
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
DeepFake
|
https://arxiv.org/abs/2102.11464v1
|
FID
|
4.29
|
Facial Recognition and Modelling > Face Swapping
|
FaceForensics++
|
FSGAN
|
https://arxiv.org/abs/2102.11464v1
|
FID
|
4.35
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CAER
|
EfficientFace
|
https://ojs.aaai.org/index.php/AAAI/article/view/16465
|
Accuracy
|
85.87
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CREMA-D
|
EmoAffectNet LSTM
|
https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656
|
UAR
|
79.0
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
^(#$!@#$)(()))******
|
S
|
https://aclanthology.org/2020.coling-main.519
|
0..5sec
|
sa
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
EmoNeXt
|
https://link.springer.com/article/10.1007/s00521-024-10938-0
|
Accuracy (8 emotion)
|
100
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
FN2EN
|
http://arxiv.org/abs/1609.06591v2
|
Accuracy (8 emotion)
|
96.8
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
FN2EN
|
http://arxiv.org/abs/1609.06591v2
|
Accuracy (7 emotion)
|
-
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
FN2EN
|
http://arxiv.org/abs/1609.06591v2
|
Accuracy (6 emotion)
|
98.6
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
PAtt-Lite
|
https://arxiv.org/abs/2306.09626v2
|
Accuracy (7 emotion)
|
100.00
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
ViT + SE
|
https://arxiv.org/abs/2107.03107v4
|
Accuracy (7 emotion)
|
99.8
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
FAN
|
https://arxiv.org/abs/1907.00193v2
|
Accuracy (7 emotion)
|
99.7
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
Nonlinear eval on SL + SSL puzzling (B0)
|
https://arxiv.org/abs/2105.06421v3
|
Accuracy (7 emotion)
|
98.23
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
CK+
|
DeepEmotion
|
http://arxiv.org/abs/1902.01019v1
|
Accuracy (7 emotion)
|
98
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Real-World Affective Faces
|
Covariance Pooling
|
http://arxiv.org/abs/1805.04855v1
|
Accuracy
|
87.0%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Real-World Affective Faces
|
Multi Label Output
|
https://arxiv.org/abs/2110.15028v1
|
Accuracy
|
79.26%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Oulu-CASIA
|
Dynamic MTL
|
https://arxiv.org/abs/1911.03281v1
|
Accuracy (10-fold)
|
89.6
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Oulu-CASIA
|
PPDN
|
http://arxiv.org/abs/1607.06997v2
|
Accuracy (10-fold)
|
84.59
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
RaFD
|
ViT + SE
|
https://arxiv.org/abs/2107.03107v4
|
Accuracy
|
87.22
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
ResNet50
|
https://arxiv.org/abs/2012.13912v1
|
Accuracy(on validation set)
|
65.5%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
LResNet50E-IR (5 models with augmentation)
|
https://arxiv.org/abs/2012.13912v1
|
Accuracy(on validation set)
|
65.5%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
EAC
|
https://arxiv.org/abs/2207.10299v2
|
Accuracy(on validation set)
|
65.32%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
LResNet50E-IR (1 model with augmentation)
|
https://arxiv.org/abs/2012.13912v1
|
Accuracy(on validation set)
|
63.7%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
LResNet50E-IR (1 model)
|
https://arxiv.org/abs/2012.13912v1
|
Accuracy(on validation set)
|
61.1%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
Multi-task EfficientNet-B0
|
https://arxiv.org/abs/2103.17107
|
Accuracy(on validation set)
|
59.27
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
resnet18_noisy
|
https://arxiv.org/abs/2008.02655v2
|
Accuracy(on validation set)
|
55.17%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Acted Facial Expressions In The Wild (AFEW)
|
resnet18
|
https://arxiv.org/abs/1907.00193v2
|
Accuracy(on validation set)
|
51.181%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
ExpW
|
ResEmoteNet
|
https://arxiv.org/abs/2409.10545v2
|
Accuracy
|
75.67
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Static Facial Expressions in the Wild
|
Covariance Pooling
|
http://arxiv.org/abs/1805.04855v1
|
Accuracy
|
58.14%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
Static Facial Expressions in the Wild
|
VGG-VD-16
|
http://arxiv.org/abs/1610.02255v1
|
Accuracy
|
54.82%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
MMI
|
DeXpression
|
http://arxiv.org/abs/1509.05371v2
|
Accuracy
|
98.63
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
MMI
|
Facial Motion Prior Network
|
https://arxiv.org/abs/1902.08788v2
|
Accuracy
|
82.74%
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
RAVDESS
|
EmoAffectNet LSTM
|
https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656
|
UAR
|
69.7
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
SAVEE
|
EmoAffectNet LSTM
|
https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656
|
UAR
|
82.8
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
PAtt-Lite
|
https://arxiv.org/abs/2306.09626v2
|
Accuracy
|
95.55
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
GReFEL
|
https://arxiv.org/abs/2410.15927v1
|
Accuracy
|
93.09
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
QCS
|
https://arxiv.org/abs/2411.01988v5
|
Accuracy
|
91.85
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
ResNet18 Dense Architecture
|
https://arxiv.org/abs/2407.04560v1
|
Accuracy
|
91.41
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
DDAMFN
|
https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C
|
Accuracy
|
90.74
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
KTN
|
https://doi.org/10.1109/TIP.2021.3049955
|
Accuracy
|
90.49
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
Vit-base + MAE
|
https://arxiv.org/abs/2207.11081v4
|
Accuracy
|
90.18
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
FER-VT
|
https://www.sciencedirect.com/science/article/pii/S0020025521008495
|
Accuracy
|
90.04
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
EAC
|
https://arxiv.org/abs/2207.10299v2
|
Accuracy
|
89.64
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
LResNet50E-IR
|
https://arxiv.org/abs/2012.13912v1
|
Accuracy
|
89.257
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
ViT-base
|
https://arxiv.org/abs/2207.11081v4
|
Accuracy
|
88.91
|
Facial Recognition and Modelling > Facial Expression Recognition (FER)
|
FER+
|
ViT-tiny
|
https://arxiv.org/abs/2207.11081v4
|
Accuracy
|
88.56
|
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