Text-to-Image synthesis is the task of generating an image according to a specific text
description. Generative Adversarial Networks have been considered the standard method …

Generating images from natural language text descriptions is an impressive feat of deep
learning. Diffusion models have enabled state-of-the-art synthesis results for various types of …

Text-to-image synthesis has recently seen significant progress thanks to large pretrained
language models, large-scale training data, and the introduction of scalable model families …

We introduce a memory-driven semi-parametric approach to text-to-image generation, which
is based on both parametric and non-parametric techniques. The non-parametric …

Generating images from natural language is one of the primary applications of recent
conditional generative models. Besides testing our ability to model conditional, highly …

Text-to-image synthesis, the process of turning words into images, opens up a world of
creative possibilities, and meets the growing need for engaging visual experiences in a …

Collecting labeled data for training of models for image recognition problems, including
handwritten text recognition (HTR), is a tedious and expensive task. Recent work on …

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 …

Text-to-image synthesis extracts the meaning from the text description and converts it into an
image correspondingly. Text-to-image synthesis is widely leveraged in many applications …

Synthesizing images from a given text description involves engaging two types of
information: the content, which includes information explicitly described in the text (eg, color …