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A Comprehensive Survey of Hypermedia System for Text- to-Image Conversion Using Generative AI
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Author(s): Tripti Majumdar (Bengal Institute of Technology, India), Sandipan Sahu (Bengal Institute of Technology, India)and Raghvendra Kumar (GIET University, India)
Copyright: 2024
Pages: 40
Source title:
The Pioneering Applications of Generative AI
Source Author(s)/Editor(s): Raghvendra Kumar (GIET University, India), Sandipan Sahu (Bengal Institute of Technology, India)and Sudipta Bhattacharya (Bengal Institute of Technology, India)
DOI: 10.4018/979-8-3693-3278-8.ch001
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Abstract
The intersection of computer vision and natural language processing (NLP) has witnessed significant advancements in recent research, particularly in the realm of converting text into meaningful images leveraging generative AI and large language models. This review work aims to comprehensively review the progress made in text-to-image conversion. The survey covers the three primary approaches in the field, namely diffusion models (DM), GAN model approaches, and autoregressive approaches. Furthermore, the authors present a comprehensive chronology of the TIG journey, encompassing its origin and the most recent developments, providing readers with a comprehensive perspective on the field's progression. The survey focuses heavily on identifying the existing constraints of DM in picture production and offers multiple research publications and their contributions in overcoming these constraints. The survey provides useful insights into the advancements in text-to-image (TIG) generation using generative AI by focusing on key difficulties and examining how different works have addressed them.
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