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Models/ViT-base + MAE

ViT-base + MAE

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

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

Computer Vision3 results

Face ReconstructiononRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081
Facial Expression Recognition (FER)onRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081
3D Face ReconstructiononRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081

Music1 result

Facial Recognition and ModellingonRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081

Methodology1 result

3DonRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081

Medical1 result

3D Face ModellingonRAF-DB
Overall Accuracy· 2022-07-22
91.07
best: 94.76 (ResEmoteNet)
SOTA
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers arXiv:2207.11081