A Novel Scheme for Generating Context-Aware Images Using Generative Artificial Intelligence

Hyunjo Kim, Jae Ho Choi, Jin Young Choi

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)31576-31588
Number of pages13
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • context-aware
  • Generative AI
  • prompt editing
  • text-to-image generation

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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