Multimodal Transformer for Parallel Concatenated Variational Autoencoders (2210.16174v1)

Abstract: In this paper, we propose a multimodal transformer using parallel concatenated architecture. Instead of using patches, we use column stripes for images in R, G, B channels as the transformer input. The column stripes keep the spatial relations of original image. We incorporate the multimodal transformer with variational autoencoder for synthetic cross-modal data generation. The multimodal transformer is designed using multiple compression matrices, and it serves as encoders for Parallel Concatenated Variational AutoEncoders (PC-VAE). The PC-VAE consists of multiple encoders, one latent space, and two decoders. The encoders are based on random Gaussian matrices and don't need any training. We propose a new loss function based on the interaction information from partial information decomposition. The interaction information evaluates the input cross-modal information and decoder output. The PC-VAE are trained via minimizing the loss function. Experiments are performed to validate the proposed multimodal transformer for PC-VAE.