Search Results for author:
Found 2 papers, 2 papers with code
Joint Multimodal Transformer for Emotion Recognition in the Wild
Multimodal emotion recognition (MMER) systems typically outperform unimodal systems by leveraging the inter- and intra-modal relationships between, e. g., visual, textual, physiological, and auditory modalities.
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition
Specifically, we propose a joint cross-attention model that relies on the complementary relationships to extract the salient features across A-V modalities, allowing for accurate prediction of continuous values of valence and arousal.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
