  State-of-the-art parametric and non-parametric style transfer approaches are
prone to either distorted local style patterns due to global statistics
alignment, or unpleasing artifacts resulting from patch mismatching. In this
paper, we study a novel semi-parametric neural style transfer framework that
alleviates the deficiency of both parametric and non-parametric stylization.
The core idea of our approach is to establish accurate and fine-grained
content-style correspondences using graph neural networks (GNNs). To this end,
we develop an elaborated GNN model with content and style local patches as the
graph vertices. The style transfer procedure is then modeled as the
attention-based heterogeneous message passing between the style and content
nodes in a learnable manner, leading to adaptive many-to-one style-content
correlations at the local patch level. In addition, an elaborated deformable
graph convolutional operation is introduced for cross-scale style-content
matching. Experimental results demonstrate that the proposed semi-parametric
image stylization approach yields encouraging results on the challenging style
patterns, preserving both global appearance and exquisite details. Furthermore,
by controlling the number of edges at the inference stage, the proposed method
also triggers novel functionalities like diversified patch-based stylization
with a single model.
