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Models/Ours (VGG-F)

Ours (VGG-F)

Reported on 17 benchmarks across 6 tasks · 1 paper · 12 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision8 results

Face ReconstructiononBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Face ReconstructiononDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Facial Expression Recognition (FER)onDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Facial Expression Recognition (FER)onBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3D Face ReconstructiononBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3D Face ReconstructiononDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Face ReconstructiononCOFW
NME (inter-ocular)· 2021-03-30
3.32
best: 5.07 (Wing (Feng et al., 2018))
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3D Face ReconstructiononCOFW
NME (inter-ocular)· 2021-03-30
3.32
best: 5.07 (Wing (Feng et al., 2018))
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554

Music3 results

Facial Recognition and ModellingonBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Facial Recognition and ModellingonDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
Facial Recognition and ModellingonCOFW
NME (inter-ocular)· 2021-03-30
3.32
best: 5.07 (Wing (Feng et al., 2018))
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554

Methodology3 results

3DonBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3DonDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3DonCOFW
NME (inter-ocular)· 2021-03-30
3.32
best: 5.07 (Wing (Feng et al., 2018))
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554

Medical3 results

3D Face ModellingonDISFA
ICC· 2021-03-30
0.598
best: 0.67 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3D Face ModellingonBP4D
ICC· 2021-03-30
0.719
best: 0.74 (Norface)
SOTA
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554
3D Face ModellingonCOFW
NME (inter-ocular)· 2021-03-30
3.32
best: 5.07 (Wing (Feng et al., 2018))
Pre-training strategies and datasets for facial representation learning arXiv:2103.16554