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 …

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

Generative adversarial networks conditioned on textual image descriptions are capable of
generating realistic-looking images. However, current methods still struggle to generate …

The goal of text-to-image synthesis is to generate a visually realistic image that matches a
given text description. In practice, the captions annotated by humans for the same image …

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 …

Text‐to‐image synthesis refers to computational methods which translate human written
textual descriptions, in the form of keywords or sentences, into images with similar semantic …

GAN's are playing an important role in creating and generating a new set of data from the
previously available content. GAN models are impressive in the results for image and video …

Due to the development of Generative Adversarial Networks (GANs), significant progress
has been achieved in text-to-image synthesis task. However, most previous works have only …