As information exists in various modalities in real world, effective interaction and fusion
among multimodal information plays a key role for the creation and perception of multimodal …

With the advent of generative adversarial networks, synthesizing images from text
descriptions has recently become an active research area. It is a flexible and intuitive way for …

We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation.
This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent …

Text-to-image generation models that generate images based on prompt descriptions have
attracted an increasing amount of attention during the past few months. Despite their …

The output of text-to-image synthesis systems should be coherent, clear, photo-realistic
scenes with high semantic fidelity to their conditioned text descriptions. Our Cross-Modal …

Human evaluation is critical for validating the performance of text-to-image generative
models, as this highly cognitive process requires deep comprehension of text and images …

Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Vector quantized diffusion (VQ-Diffusion) is a powerful generative model for text-to-image
synthesis, but sometimes can still generate low-quality samples or weakly correlated images …

Despite the cross-modal text-to-imagesynthesis task has achieved great success, most of
the latest works in this field are based on the network architectures proposed by …