Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include GAN and deepfake detection.

Research and Interests

• Computer Vision

Publications

• Honglin Lin, Mengmeng Wang, Yong Liu, and Jiaxin Kou. Correlation-based and content-enhanced network for video style transfer. Pattern Analysis and Applications, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  Artistic style transfer aims to migrate the style pattern from a referenced style image to a given content image, which has achieved significant advances in recent years. However, producing temporally coherent and visually pleasing stylized frames is still challenging. Although existing works have made some effort, they rely on the inefficient optical flow or other cumbersome operations to model spatiotemporal information. In this paper, we propose an arbitrary video style transfer network that can generate consistent results with reasonable style patterns and clear content structure. We adopt multi-channel correlation module to render the input images stably according to cross-domain feature correlation. Meanwhile, Earth Movers’ Distance is used to capture the main characteristics of style images. To maintain the semantic structure during the stylization, we also employ the AdaIN-based skip connections and self-similarity loss, which can further improve the temporal consistency. Qualitative and quantitative experiments have demonstrated the effectiveness of our framework.

  @article{lin2022cbc,
  title = {Correlation-based and content-enhanced network for video style transfer},
  author = {Honglin Lin and Mengmeng Wang and Yong Liu and Jiaxin Kou},
  year = 2022,
  journal = {Pattern Analysis and Applications},
  doi = {10.1007/s10044-022-01106-y},
  abstract = {Artistic style transfer aims to migrate the style pattern from a referenced style image to a given content image, which has achieved significant advances in recent years. However, producing temporally coherent and visually pleasing stylized frames is still challenging. Although existing works have made some effort, they rely on the inefficient optical flow or other cumbersome operations to model spatiotemporal information. In this paper, we propose an arbitrary video style transfer network that can generate consistent results with reasonable style patterns and clear content structure. We adopt multi-channel correlation module to render the input images stably according to cross-domain feature correlation. Meanwhile, Earth Movers' Distance is used to capture the main characteristics of style images. To maintain the semantic structure during the stylization, we also employ the AdaIN-based skip connections and self-similarity loss, which can further improve the temporal consistency. Qualitative and quantitative experiments have demonstrated the effectiveness of our framework.}
  }