Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Incomplete Data Scenarios

arXiv:2311.16114v2 Announce Type: replace-cross
Abstract: Multimodal emotion recognition (MER) in practical scenarios is significantly challenged by the presence of missing or incomplete data across different modalities. To overcome these challenges, researchers have aimed to simulate incomplete conditions during the training phase to enhance the system's overall robustness. Traditional methods have often involved discarding data or substituting data segments with zero vectors to approximate these incompletenesses. However, such approaches neither accurately represent real-world conditions nor adequately address the issue of noisy …

abstract arxiv challenges cs.ai cs.cv cs.lg data emotion incomplete data multimodal noise practical recognition representation researchers robust robustness training type