Open AccessA novel scheme for generating context-aware images using generative Artificial Intelligence
Author(s) -
Hyunjo Kim,
Jae-Ho Choi,
Jin-Young Choi
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3368871
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Humans possess the remarkable capacity to comprehend narratives presented in text and subsequently conjure associated mental images through their imagination. This cognitive ability enhances their grasp of the content and augments their overall enjoyment. Consequently, the development of an automated system aimed at producing visually faithful images based on textual descriptions, often referred to as the text-to-image task, stands as a profoundly meaningful endeavor. For this reason, a variety of text-to-image generating artificial intelligences (AIs) have been devised until now. Nevertheless, the generative AIs introduced thus far encounter an issue wherein they struggle to uphold the coherence of input sentences, particularly when multiple sentences are provided. Within this paper, we present a remedy to this challenge through the application of prompt editing. Furthermore, our experimental results substantiate that our proposed solution more effectively preserves contextual coherence among the generated images in comparison to other preexisting generative artificial intelligence models. The experimental results demonstrate that the proposed scheme improves performance by at least 30 percent in terms of the similarity of the generated image and by 130 percent in terms of ROUGE recall .