Valence Arousal Estimation

Valence-arousal estimation aims to automatically assess the emotional state of individuals by quantifying their valence (positivity/negativity) and arousal (activation/deactivation) levels. Current research heavily utilizes deep learning models, often incorporating multimodal data (visual, audio, physiological signals) and advanced architectures like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs) for improved accuracy and robustness, particularly in challenging "in-the-wild" settings. This field is crucial for advancing human-computer interaction, clinical diagnostics (e.g., sleep disorder detection), and other applications requiring real-time emotion understanding, with ongoing efforts focused on improving model fairness, generalizability, and efficiency.