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Facial Expression Recognition On Raf Db

评估指标

Overall Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称
Overall Accuracy
Paper TitleRepository
DDAMFN++92.34A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
GReFEL92.47GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution-
ViT-tiny87.03Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers-
DAN89.70Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
FMAE93.09Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
FaceBehaviorNet-Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study-
ViT-base + MAE91.07Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers-
MT-ArcVGG-Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace-
FerNeXt88.56FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
S2D92.57From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
C-EXPR-NET-Multi-Label Compound Expression Recognition: C-EXPR Database & Network-
DACL (ResNet-18)87.78Facial Expression Recognition in the Wild via Deep Attentive Center Loss
BTN92.54Batch Transformer: Look for Attention in Batch-
EfficientFace88.36Robust Lightweight Facial Expression Recognition Network with Label Distribution Training
RAN (ResNet-18)86.9Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
RUL (ResNet-18)88.98Relative Uncertainty Learning for Facial Expression Recognition
POSTER++92.21POSTER++: A simpler and stronger facial expression recognition network
C MT VGGFACE-Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond-
APViT91.98Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
MA-Net88.36Learning Deep Global Multi-scale and Local Attention Features for Facial Expression Recognition in the Wild
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