Recent neural style transfer frameworks have obtained astonishing visual
quality and flexibility in Single-style Transfer (SST), but little attention
has been paid to Multi-style Transfer (MST) which refers to simultaneously
transferring multiple styles to the same image. Compared to SST, MST has the
potential to create more diverse and visually pleasing stylization results. In
this paper, we propose the first MST framework to automatically incorporate
multiple styles into one result based on regional semantics. We first improve
the existing SST backbone network by introducing a novel multi-level feature
fusion module and a patch attention module to achieve better semantic
correspondences and preserve richer style details. For MST, we designed a
conceptually simple yet effective region-based style fusion module to insert
into the backbone. It assigns corresponding styles to content regions based on
semantic matching, and then seamlessly combines multiple styles together.
Comprehensive evaluations demonstrate that our framework outperforms existing
works of SST and MST.

本文提出了一种基于区域语义的多样式迁移框架，其中引入了新的多级特征融合模块和补丁注意力模块来实现更好的语义对应和保留更丰富的样式细节，并使用区域风格融合模块将多个样式无缝地结合在一起，实现更丰富、更美观的多样式迁移结果。