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Improvement of Background Inpainting using Binary Masking of a Generated Image
Jihoon Lee, Chan Ho Bae, Seunghun Lee, Myung-Seok Choi, Ryong Lee, Sangtae Ahn
http://doi.org/10.5626/JOK.2024.51.6.537
Recently, image generation technology has been rapidly advancing in the field of deep learning. One of the most effective ways to represent images is by using text prompts to generate them. The performance of models that generate images using this technique is outstanding. However, it is not easy to naturally change specific parts of an image using only text prompts. This is considered a typical problem with conventional image generation models. Thus, in this study, we developed a background inpainting technique that extracts text for each area of an image and uses it as a basis to seamlessly change the background while preserving the objects in the image. In particular, the background transformation inpainting technique developed in this study has the advantage of not only transforming a single image but also rapidly transforming multiple images. Therefore, the proposed text prompt-based image style transfer can be used in fields with limited data for training, and the technique could enhance the performance of models through image augmentation.
Automatic Generation of Custom Advertisement Messages based on Literacy Styles of Classified Personality Types
Jimin Seong, Yunjong Choi, Doyeon Kwak, Hansaem Kim
http://doi.org/10.5626/JOK.2024.51.1.23
This study introduces a novel framework that defines marketing styles based on the MBTI personality types, and presents a machine learning technique to generate customized advertising messages aligned to these types. We use the BART algorithm to synthesize customized advertising content by training on the advertisement texts incorporating personality type prefixes. Our experiments confirm the model’s efficacy in transforming generic advertising copy into custom messages that embody the distinct style characteristics of each personality type, via prefix manipulation. Theoretically, our research establishes the relationship between style characteristics and personality types; practically, it provides the technique to fine-tune a language model to generate advertising messages that align with specific personality types. Moreover, this research serves as a foundational work for systematizing and replicating stylistic differences across various languages and regions.
Style Transfer for Chat Language using Unsupervised Machine Translation
Youngjun Jung, Changki Lee, Jeongin Hwang, Hyungjong Noh
http://doi.org/10.5626/JOK.2023.50.1.19
Style transfer is the task of generating text of a target style while maintaining content of given text written in a source style. In general, it is assumed that the content is an invariant and the style is variable when the style of the text is transferred. However, in the case of chat language, there is a problem in that it is not well trained by existing style transfer model. In this paper, we proposed a method of transfer chat language into written language using a style transfer model with unsupervised machine translation. This study shows that it is possible to construct a word transfer dictionary between styles that can be used for style transfer by utilizing transferred results. Additionally, it shows that transferred results can be improved by applying a filtering method to transferred result pair so that only well transferred results can be used and by training the style transfer model using a supervised learning method with filtered results.
Language Style Transfer Based on Surface-Level Neutralization
Wooyong Choi, Yunseok Noh, Seyoung Park
http://doi.org/10.5626/JOK.2020.47.2.162
Two main concerns of language style transfer such as sentiment transfer are neutralization of a stylized sentence and re-stylization of the neutralized sentence with a target style. Generally, neutralization is accomplished by learning a neutralized latent space by adversarial learning. However, this neutralization method suffers from the difficulty of maintaining the original content after style transfer. In this paper, we propose a two-step language style transfer method comprised of a surface-level neutralization that removes style words and a target-style word prediction for the removed words. For this, a self-attentive style classifier and style-specific word predictors are used for the surface neutralization and style word generation, respectively. To evaluate the proposed method, several experiments of language style transfer were conducted with Yelp and Amazon review datasets and Caption dataset. As a result, the proposed method shows superior performance over baseline methods on various evaluation metrics including automatic and human evaluations.
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