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Related Experiment Video

Updated: Jul 31, 2025

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CSAST: Content self-supervised and style contrastive learning for arbitrary style transfer.

Yuqi Zhang1, Yingjie Tian2, Junjie Hou3

  • 1School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 7, 2023
PubMed
Summary

This study introduces novel self-supervised and contrastive learning methods to enhance arbitrary artistic style transfer. The approach improves content preservation and style accuracy for both images and videos.

Keywords:
Arbitrary artistic styleContent self-supervised learningStyle contrastive learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep neural networks have advanced arbitrary artistic style transfer.
  • Existing methods struggle with content preservation and style accuracy due to content-style conflict.

Purpose of the Study:

  • To improve content preservation and style translation in arbitrary style transfer.
  • To address the inherent conflict between content and style in image and video stylization.

Main Methods:

  • Introduced content self-supervised learning based on perceptual similarity of transformed images.
  • Developed style contrastive learning using Gram matrices to refine style representations.
  • Applied these methods to arbitrary style transfer for images and videos.

Main Results:

  • Content self-supervised learning enhanced content consistency and reduced artifacts.
  • Style contrastive learning improved style accuracy and visual appeal.
  • The combined approach demonstrated superior performance in qualitative and quantitative experiments for both images and videos, including improved inter-frame continuity for videos.

Conclusions:

  • The proposed content self-supervised and style contrastive learning significantly enhances arbitrary style transfer.
  • The method effectively balances content preservation and style translation, offering improved visual quality for images and videos.
  • This approach shows particular promise for video style transfer due to enhanced temporal consistency.