Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM25320
Volume:
7 |
8 |
Aug 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
This survey reviews text-to-image generation by using different approaches. One of the approaches identified in this study is Cross-modal Semantic Matching Generative Adversarial Networks (CSM-GAN), which is used to increase semantic consistency between text descriptions and synthesised pictures for fine-grained text-to-image creation. This includes other two modules, Text Encoder Module and Textual-Visual Semantic Matching Module. We further discussed about Imagen which is a text-to-image diffusion model with photorealism and deep language understanding, which is used on the COCO dataset. Lastly, we discussed about Text to image synthesis used to automates image generation using conditional generative models and GAN, enhancing artificial intelligence and deep learning. Based on these approaches we present a review of text to image generation using generative AI.
KEYWORDS
Publication Date
8/21/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...