Abstract
In recent years, deep learning has attracted attention not only as a method on image recognition but also as a technique for image generation and transformation. Above all, a method called Style Transfer is drawing much attention which can integrate two photos into one integrated photo regarding their content and style. Although many extended works including Fast Style Transfer have been proposed so far, all the extended methods including original one require a style image to modify the style of an input image. In this paper, we propose to use words expressing photo styles instead of using style images for neural image style transfer. In our method, we take into account the content of an input image to be stylized to decide a style for style transfer in addition to a given word. We implemented the propose method by modifying the network for arbitrary neural artistic stylization. By the experiments, we show that the proposed method has ability to change the style of an input image taking account of both a given word.
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Acknowledgments
This work was supported by JSPS KAKENHI Grant Number 15H05915, 17H01745, 17H05972, 17H06026 and 17H06100.
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Sugiyama, Y., Yanai, K. (2019). Word-Conditioned Image Style Transfer. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_8
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DOI: https://doi.org/10.1007/978-3-030-21074-8_8
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