GAU-Nets: Graph Attention U-Nets for Image Classification

Abstract

Graph neural network is a research hotspot in the field of deep learning recently, and its application has become more and more extensive. A new graph neural network model is proposed, Graph Attention U-Nets(GAU-Nets for short), which has strong processing capabilities for graph structures. In GAU-Nets, the graph data structure is preliminarily processed through GCN, and then extracted the features by the graph pooling and Unpooling blocks. We innovatively added the attention mechanism to GAU-Nets to avoid forgetting the important information. We have done a lot of image classification experiments on MS-COCO and other datasets. The experimental results prove that GAU-Nets performs better than other traditional graph neural network models. Without bells and whistles, our GAU-Nets method has an accuracy of 69.1% on the MS-COCO data set and 82.1% on the VOC 2007 data set, which has surpassed all benchmark methods.