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Models/Emotion-GCN

Emotion-GCN

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 ReconstructiononAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487
Facial Expression Recognition (FER)onAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487
3D Face ReconstructiononAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487

Music1 result

Facial Recognition and ModellingonAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487

Methodology1 result

3DonAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487

Medical1 result

3D Face ModellingonAffectNet
Accuracy (7 emotion)· 2021-06-07
66.46
best: 72.93 (ResEmoteNet)
SOTA
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition arXiv:2106.03487