InDecGAN: Learning to Generate Complex Images From Captions via Independent Object-Level Decomposition and Enhancement-Reference-Cited by-同舟云学术

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InDecGAN: Learning to Generate Complex Images From Captions via Independent Object-Level Decomposition and Enhancement

Published:2023 Issue: Volume:25 Page:8279-8293
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Cheng Jun¹^ORCID,Wu Fuxiang¹^ORCID,Liu Liu²^ORCID,Zhang Qieshi¹^ORCID,Rutkowski Leszek³^ORCID,Tao Dacheng²^ORCID

Affiliation:

1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Beijing, China

2. School of Computer Science, Faculty of Engineering, The University of Sydney, Darlington, NSW, Australia

3. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

Funder

National Natural Science Foundation of China

Australian Research Council Project

National Natural Science Foundation of Guangdong Province

Shenzhen Technology Project

3D Digital Media Technology Engineering Laboratory

CAS Key Technology Talent Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Media Technology,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6046/10016790/10068791.pdf?arnumber=10068791

Reference57 articles.

1. Generative adversarial text to image synthesis;Reed,2016

2. Image Generation from Scene Graphs

3. Chatpainter: Improving text to image generation using dialogue;Sharma,2018

4. Text2Scene: Generating Compositional Scenes From Textual Descriptions

5. StoryGAN: A Sequential Conditional GAN for Story Visualization

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