Weakly-supervised Learning for Fine-grained Emotion Recognition using Physiological Signals

Instead of predicting just one emotion for one activity (e.g., video watching), fine-grained emotion recognition enables more temporally precise recognition. Previous works on fine-grained emotion recognition require segment-by-segment, fine-grained emotion labels to train the recognition algorithm. However, experiments to collect these...

Few-shot Learning for Fine-grained Emotion Recognition using Physiological Signals

Fine-grained emotion recognition can model the temporal dynamics of emotions, which is more precise than predicting one emotion retrospectively for an activity (e.g., video clip watching). Previous works require large amounts of continuously annotated data to train an accurate recognition model, however experiments to collect such large...

Corrnet: Fine-grained emotion recognition for video watching using wearable physiological sensors

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion...