ARGen uses AU-guided prompts and a reinforcement-learned diffusion strategy to synthesize scarce-class facial expression videos that improve dynamic emotion recognition.
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Synthetic datasets created via diffusion models, GAN editing, and pseudo-labeling can substitute for or augment real data to improve facial expression recognition while respecting privacy constraints.
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.
citing papers explorer
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ARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception
ARGen uses AU-guided prompts and a reinforcement-learned diffusion strategy to synthesize scarce-class facial expression videos that improve dynamic emotion recognition.
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On Applicability of Synthetic Datasets for Facial Expression Recognition
Synthetic datasets created via diffusion models, GAN editing, and pseudo-labeling can substitute for or augment real data to improve facial expression recognition while respecting privacy constraints.
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Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.