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Models/RAN (ResNet-18)

RAN (ResNet-18)

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· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075
Facial Expression Recognition (FER)onRAF-DB
Overall Accuracy· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075
3D Face ReconstructiononRAF-DB
Overall Accuracy· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075

Music1 result

Facial Recognition and ModellingonRAF-DB
Overall Accuracy· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075

Methodology1 result

3DonRAF-DB
Overall Accuracy· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075

Medical1 result

3D Face ModellingonRAF-DB
Overall Accuracy· uses extra data· 2019-05-10
86.9
best: 94.76 (ResEmoteNet)
SOTA
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition arXiv:1905.04075