Cross-Modal Dynamic Transfer Learning for Multimodal Emotion Recognition

Multimodal Emotion Recognition is an important research area for developing human-centric applications, especially in the context of video platforms. Most existing models have attempted to develop sophisticated fusion techniques to integrate heterogeneous features from different modalities. However, these fusion methods can affect performance since not all modalities help figure out the semantic alignment for emotion prediction. We observed that the 8.0% of misclassified instances’ performance is improved for the existing fusion model when one of the input modalities is masked. Based on this observation, we propose a representation learning method called Cross-modal DynAmic Transfer learning (CDaT), which dynamically filters the low-confident modality and complements it with the high-confident modality using uni-modal masking and cross-modal representation transfer learning. We train an auxiliary network that learns model confidence scores to determine which modality is low-confident and how much the transfer should occur from other modalities. Furthermore, it can be used with any fusion model in a model-agnostic way because it leverages transfer between low-level uni-modal information via probabilistic knowledge transfer loss. Experiments have demonstrated the effect of CDaT with four different state-of-the-art fusion models on the CMU-MOSEI and IEMOCAP datasets for emotion recognition.