Learning to Fuse Latent Representations for Multimodal Data

Multimodal learning leverages data from different modalities to improve the performance of a trained model. Typically, latent representations extracted from multimodal data are provided via direct feature fusion for end-to-end training of a deep neural network towards a specific task. However, the informativeness of the different data modalities can easily vary across a collected dataset. As such, naively or directly fusing the latent representations obtained for one modality and the other, as is commonly done in state-of-the-art works, may burden the model in finding concise representations that are indeed useful for learning. In this paper, we propose to instead learn the fusion of latent representations for multimodal data by using a modality gating mechanism that allows the dynamic weighting of extracted latent representations based on their informativness. Extensive experiments using the BU-3DFE dataset for facial expression recognition and the Washington object classification multimodal RGB-D dataset show that learning the fusion of the latent representations for different data modalities leads to improved model generalization than the conventional naive fusion method.