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Found 3 papers, 2 papers with code
Efficient Labelling of Affective Video Datasets via Few-Shot & Multi-Task Contrastive Learning
Whilst deep learning techniques have achieved excellent emotion prediction, they still require large amounts of labelled training data, which are (a) onerous and tedious to compile, and (b) prone to errors and biases.
A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference
Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention.
Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals
Employing traditional machine learning and deep learning methods, we explore (a) dimensional and categorical emotion recognition, and (b) PD vs HC classification from emotional EEG signals.
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