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Found 4 papers, 2 papers with code
Class adaptive threshold and negative class guided noisy annotation robust Facial Expression Recognition
Instead of discarding the non-confident samples, we impose consistency in the negative classes of those non-confident samples to guide the model to learn better in the positive class.
Facial Expression Recognition
Facial Expression Recognition (FER)
ABAW : Facial Expression Recognition in the wild
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation Challenge.
Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition
To handle noisy annotations, we propose a dynamic FER learning framework (DNFER) in which clean samples are selected based on dynamic class specific threshold during training.
Facial Expression Recognition
Facial Expression Recognition (FER)
SS-MFAR : Semi-supervised Multi-task Facial Affect Recognition
Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc.
