Recently, patch-wise contrastive learning is drawing attention for the image
translation by exploring the semantic correspondence between the input and
output images. To further explore the patch-wise topology for high-level
semantic understanding, here we exploit the graph neural network to capture the
topology-aware features. Specifically, we construct the graph based on the
patch-wise similarity from a pretrained encoder, whose adjacency matrix is
shared to enhance the consistency of patch-wise relation between the input and
the output. Then, we obtain the node feature from the graph neural network, and
enhance the correspondence between the nodes by increasing mutual information
using the contrastive loss. In order to capture the hierarchical semantic
structure, we further propose the graph pooling. Experimental results
demonstrate the state-of-art results for the image translation thanks to the
semantic encoding by the constructed graphs.

利用图神经网络构建基于补丁相似度的图，提出补丁级别的对比损失和图池化方法，实现图像翻译的语义编码与语义结构的提取。