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Volume 37 Issue 3
May  2022
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Hua-Peng Wei, Ying-Ying Deng, Fan Tang, Xing-Jia Pan, Wei-Ming Dong. A Comparative Study of CNN- and Transformer-Based Visual Style Transfer[J]. Journal of Computer Science and Technology, 2022, 37(3): 601-614. DOI: 10.1007/s11390-022-2140-7
Citation: Hua-Peng Wei, Ying-Ying Deng, Fan Tang, Xing-Jia Pan, Wei-Ming Dong. A Comparative Study of CNN- and Transformer-Based Visual Style Transfer[J]. Journal of Computer Science and Technology, 2022, 37(3): 601-614. DOI: 10.1007/s11390-022-2140-7
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A Comparative Study of CNN- and Transformer-Based Visual Style Transfer

  • 1School of Artificial Intelligence, Jilin University, Changchun 130012, China
    2National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    3Youtu Laboratory, Tencent Incorporated, Shanghai 200233, China

Funds: The work was supported by the National Key Research and Development Program of China under Grant No. 2020AAA0106200, the National Natural Science Foundation of China under Grant Nos. 62102162, 61832016, U20B2070, and 6210070958, the CASIA-Tencent Youtu Joint Research Project, and the Open Projects Program of the National Laboratory of Pattern Recognition.
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  • Author Bio:

    Fan Tang is an assistant professor in the School of Artificial Intelligence, Jilin University, Changchun. He received his Ph.D. degree from the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, in 2019. His research interests include computer graphics, computer vision, and machine learning.

  • Corresponding author:

    Fan Tang E-mail: tangfan@jlu.edu.cn

  • Received Date: January 04, 2022
  • Revised Date: April 11, 2022
  • Accepted Date: April 23, 2022
  • Published Date: May 29, 2022
    Abstract
  • Vision Transformer has shown impressive performance on the image classification tasks. Observing that most existing visual style transfer (VST) algorithms are based on the texture-biased convolution neural network (CNN), here raises the question of whether the shape-biased Vision Transformer can perform style transfer as CNN. In this work, we focus on comparing and analyzing the shape bias between CNN- and transformer-based models from the view of VST tasks. For comprehensive comparisons, we propose three kinds of transformer-based visual style transfer (Tr-VST) methods (Tr-NST for optimization-based VST, Tr-WCT for reconstruction-based VST and Tr-AdaIN for perceptual-based VST). By engaging three mainstream VST methods in the transformer pipeline, we show that transformer-based models pre-trained on ImageNet are not proper for style transfer methods. Due to the strong shape bias of the transformer-based models, these Tr-VST methods cannot render style patterns. We further analyze the shape bias by considering the influence of the learned parameters and the structure design. Results prove that with proper style supervision, the transformer can learn similar texture-biased features as CNN does. With the reduced shape bias in the transformer encoder, Tr-VST methods can generate higher-quality results compared with state-of-the-art VST methods.
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