StyTr 2 : Image Style Transfer with Transformers

The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural style transfer methods face biased content representation. To address this critical issue, we take long-range dependencies of input images into account for image style transfer by proposing a transformer-based approach called StyTr2. In contrast with visual transformers for other vision tasks, StyTr2 contains two different transformer encoders to generate domain-specific sequences for content and…

• NN
  National Natural Science Foundation of China

  Award: 61832016,U20B2070,6210070958,62102162