Top Research Papers on Text to Image Generation

This work introduces a controlled chaos system, where a stalling generator network and a disoriented discriminator network co-exist, and explores the potential of GANs for jumbled up art creation, data manipulation, and image confusion, opening a new frontier in GAN applications.

A novel method to generate text image is presented, and some results are shown to demonstrate the generation of text image with the proposed method.

For the last three years diffusion denoising approach with a score-based loss function became notable as well: many researches tackling problem of image generation report SOTA results.

This work creates an end-to-end solution that can generate artistic images from text descriptions due to the lack of datasets with paired text description and artistic images.

This research addresses the challenge by leveraging AI techniques to develop a robust Text-to-Image Generation system that translating text prompt into accurate, high-quality images.

This work develops Surgical Imagen, a diffusion-based generative model that is developed to generate photorealistic and activity-aligned surgical images from triplet-based textual prompts, and designs an instrument-based class balancing technique to counteract data imbalance and skewness, improving training convergence.

The paper argues that the current product-centered view of creativity falls short in the context of text-to-image generation, and provides a high-level summary of this online ecosystem drawing on Rhodes’ conceptual four P model of creativity.

The first document assesses the state of the art, identifying four kinds of technical developments which will shape the art in the coming decade: linguistically justified grammars, knowledge represen ta t ion methods, models of the reader, and models of discourse.

The first document assesses the state of the art, identifying four kinds of technical developments which will shape the art in the coming decade: linguistically justified grammars, knowledge representation methods, models of the reader, and models of discourse.

A new image generation method that uses segmentation and text as input that is capable of handling complex layouts and maintaining natural object structure even with a large number of objects is proposed and validated.

Why explanation is a crucial feature of expert systems, how text generation can be used within database systems to familiarize users with the database, and where text generationCan aid communication with problem-solving systems are shown.

This paper presents a progressive model for high-fidelity text-to-image generation that produces significantly better results compared with the previous VQ-AR method in FID score across a wide variety of categories and aspects.

This paper proposes an approach a framework that will accept text input from the user about the fashion pattern and the model will generate images of fashion clothing based on the text input, which can assist people be their own designers for creating a range offashion clothing for themselves using the power of Deep Learning and Generative Adversarial Networks.

Using this Android application project, the authors can enhance their imaginative ideas into a realistic one and it is a friendly app where they won't face any issues in pictures.

FIELD: image generation. SUBSTANCE: image generation element includes chemical compound in crystalline form, which transforms to amorphous form, which has its own color, different from color of crystalline form. Image generation temperature mainly does not depend on time of heating. EFFECT: increased efficiency. 2 cl, 8 app, 3 dwg

Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information, significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos.

It is found that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk, which shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.

Apresenta-se o resultado de um protótipo didático elaborado no  Profletras, apoiado nos princípios da pedagogia dos multiletramentos (Rojo, 2012), na prática da leitura e análise de textos-enunciados multissemióticos na perspectiva interacionista de linguagem (Bakhtin, 2003; Volochinov, 1992) e na concepção interacional de leitura (Leffa, 1999; Kleiman, 2019) sob o viés da interdisciplinaridade entre as disciplinas de Língua Portuguesa e Geografia, destinado a uma turma de 8º ano do Ensino Fundamental II. O objetivo foi contribuir para a construção do sujeito leitor, abrangendo a comunicação e...

This research work carried out a research work where it concentrated on developing the generative adversarial network (GAN), a deep learning-based architecture that consistently creates realistic graphics based on predetermined criteria.

Experimental results demonstrate that the proposed memory-driven semi-parametric approach to text-to-image generation produces more realistic images than purely parametric approaches, in terms of both visual fidelity and text-image semantic consistency.