Robust Emotion Recognition in Context Debiasing

arXiv:2403.05963v1 Announce Type: cross
Abstract: Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation, causing severe performance bottlenecks and confounding valuable …

abstract applications arxiv bias challenge computing context cs.cv cs.lg diverse emotion ensemble environments extract interference person practical recognition robust state type