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.

本文提出了一种半参数神经风格迁移框架，使用图神经网络和基于注意力的异构消息传递来建立准确和细粒度的内容风格对应关系，在局部补偿全局统计特征的同时消除了补丁拟合不足带来的瑕疵，并引入了变形图卷积操作进行跨尺度风格匹配，实现了单一模型多样化补丁风格化。实验结果表明，该方法在保留全局外观和精致细节方面取得了令人鼓舞的效果。

学习图神经网络用于图像风格转移