  Graph convolutional networks (GCN) is widely used to handle irregular data
since it updates node features by using the structure information of graph.
With the help of iterated GCN, high-order information can be obtained to
further enhance the representation of nodes. However, how to apply GCN to
structured data (such as pictures) has not been deeply studied. In this paper,
we explore the application of graph attention networks (GAT) in image feature
extraction. First of all, we propose a novel graph generation algorithm to
convert images into graphs through matrix transformation. It is one magnitude
faster than the algorithm based on K Nearest Neighbors (KNN). Then, GAT is used
on the generated graph to update the node features. Thus, a more robust
representation is obtained. These two steps are combined into a module called
pixel-wise graph attention module (PGA). Since the graph obtained by our graph
generation algorithm can still be transformed into a picture after processing,
PGA can be well combined with CNN. Based on these two modules, we consulted the
ResNet and design a pixel-wise graph attention network (PGANet). The PGANet is
applied to the task of person re-identification in the datasets Market1501,
DukeMTMC-reID and Occluded-DukeMTMC (outperforms state-of-the-art by 0.8\%,
1.1\% and 11\% respectively, in mAP scores). Experiment results show that it
achieves the state-of-the-art performance.
\href{https://github.com/wenyu1009/PGANet}{The code is available here}.
