Multimedia applications of computer vision

Asmuth, Jane C.; Dixon, Douglas; Hanna, Keith; Hsu, Sheng Teng; Kumar, Raman; Paragano, V.; Pope, Art; Samarasekera, Supun; Sawhney, Harpreet

doi:10.1109/acv.1998.732910

Cited by 3 publications

(1 citation statement)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision applications have strong potential for industries including but not limited to the medical, government, military, entertainment, and online social media fields (Asmuth et al, 1998;Fang, Wang, & Tang, 2018;Hong, Yang, Choi, & Lee, 2018;Mansimov, Parisotto, Ba, & Salakhutdinov, 2016;Nie, Trullo, Petitjean, Ruan, & Shen, 2016;Wu et al, 2017). Text-to-image synthesis is one such application in computer vision AI that has become the main focus in recent years due to its potential for providing beneficial properties and opportunities for a wide range of applicable areas.…”

Section: Text-to-image Synthesis Applicationsmentioning

confidence: 99%

A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis

Agnese

Herrera

Tao

et al. 2020

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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 meaning to the text. In earlier research, image synthesis relied mainly on word to image correlation analysis combined with supervised methods to find best alignment of the visual content matching to the text. Recent progress in deep learning (DL) has brought a new set of unsupervised deep learning methods, particularly deep generative models which are able to generate realistic visual images using suitably trained neural network models. The change of direction from the computer vision based approaches to artificial intelligence (AI) driven methods ignited the intense interest in industry, such as virtual reality, recreational & professional (eSports) gaming, and computer-aided design etc., to automatically generate compelling images from text-based natural language descriptions. In this paper, we review the most recent development in the text-to-image synthesis research domain. Our goal is to provide value by delivering a comparative review of the state-of-the-art models in terms of their architecture and design. Our survey first introduces image synthesis and its challenges, and then reviews key concepts such as generative adversarial networks (GANs) and deep convolutional encoderdecoder neural networks (DCNN). After that, we propose a taxonomy to summarize GAN based text-toimage synthesis into four major categories: Semantic Enhancement GANs, Resolution Enhancement GANs, Diversity Enhancement GANS, and Motion Enhancement GANs. We elaborate the main objective of each group, and further review typical GAN architectures in each group. The taxonomy and the review outline the techniques and the evolution of different approaches, and eventually provide a clear roadmap to summarize the list of contemporaneous solutions that utilize GANs and DCNNs to generate enthralling results in categories such as human faces, birds, flowers, room interiors, object reconstruction from edge maps (games) etc. The survey will conclude with a comparison of the proposed solutions, challenges that remain unresolved, and future developments in the text-to-image synthesis domain.

show abstract