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Text-Guided Style Transfer-Based Image Manipulation Using Multimodal Generative Models

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Title: Text-Guided Style Transfer-Based Image Manipulation Using Multimodal Generative Models
Authors: Togo, Ren Browse this author
Kotera, Megumi Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Task analysis
Feature extraction
Deep learning
Electronic mail
Computational modeling
Style transfer
image manipulation
text-to-image synthesis
aesthetic analysis
generative model
Issue Date: 8-Jun-2021
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Access
Volume: 9
Start Page: 64860
End Page: 64870
Publisher DOI: 10.1109/ACCESS.2021.3069876
Abstract: A new style transfer-based image manipulation framework combining generative networks and style transfer networks is presented in this paper. Unlike conventional style transfer tasks, we tackle a new task, text-guided image manipulation. We realize style transfer-based image manipulation that does not require any reference style images and generate a style image from the user's input sentence. In our method, since an initial reference input sentence for a content image can automatically be given by an image-to-text model, the user only needs to update the reference sentence. This scheme can help users when they do not have any images representing the desired style. Although this text-guided image manipulation is a new challenging task, quantitative and qualitative comparisons showed the superiority of our method.
Type: article
Appears in Collections:数理・データサイエンス教育研究センター (Education and Research Center for Mathematical and Data Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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