Recognizing Geographical Locations using a GAN-Based Text-To-Image Approach

dc.contributor.authorIbrahim, Dina M.
dc.contributor.authorAl-Shargabi, Amal A.
dc.date.acceptance2024-09-30
dc.date.accessioned2024-10-30T14:46:42Z
dc.date.available2024-10-30T14:46:42Z
dc.date.issued2025
dc.descriptionopen access article
dc.description.abstractGenerating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in Machine Learning Algorithms. Using text as a source, Generative Adversarial Networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location services. To produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image-text matching loss needed to train the AttnGAN’s generator is computed using our multimodal similarity model. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset comprises photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
dc.funderOther external funder (please detail below)
dc.funder.otherQassim University
dc.identifier.citationIbrahim, Dina M. and Al-Shargabi, Amal A. (2025) Recognizing Geographical Locations using a GAN-Based Text-To-Image Approach. Indonesian Journal of Electrical Engineering and Computer Science
dc.identifier.issn2502-4752
dc.identifier.urihttps://hdl.handle.net/2086/24437
dc.language.isoen
dc.peerreviewedYes
dc.projectid2023-SDG-1-BSRC36794
dc.publisherInstitute of Advanced Engineering and Science (IAES)
dc.researchinstitute.instituteInstitute of Digital Research, Communication and Responsible Innovation
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectText-to-Image
dc.subjectText reading
dc.subjectGenerative Adversarial Network; GANs
dc.subjectAttnGAN model
dc.subjectLocation-based services
dc.subjectRoad infrastructure
dc.subjectDeep learning
dc.titleRecognizing Geographical Locations using a GAN-Based Text-To-Image Approach
dc.typeArticle

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