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Facial Expression Recognition On Raf Db

평가 지표

Overall Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

Paper Title
ResEmoteNet94.76ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
FMAE93.09Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
QCS93.02QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
Norface92.97Norface: Improving Facial Expression Analysis by Identity Normalization
S2D92.57From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos
BTN92.54Batch Transformer: Look for Attention in Batch
GReFEL92.47GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
DDAMFN++92.34A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
DCJT92.24Joint Training on Multiple Datasets With Inconsistent Labeling Criteria for Facial Expression Recognition
POSTER++92.21POSTER++: A simpler and stronger facial expression recognition network
APViT91.98Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
DDAMFN91.35A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
ViT-base + MAE91.07Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
LFNSB91.07A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss
EAC(ResNet-50)90.35Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
Ada-DF90.04A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition
DAN89.70Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition
RUL (ResNet-18)88.98Relative Uncertainty Learning for Facial Expression Recognition
PSR88.98Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition
FerNeXt88.56FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
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