Part-GCNet: Partitioning Graph Convolutional Network for Multi-Label Recognition

During the rapid development of deep learning, the multi-label recognition task has achieved pretty performance. Recently, the emergence of graph convolution network (GCN) has further improved the accuracy of multi-label recognition. However, in the learning process, how to better represent the feature information of labels and innovatively design structures to obtain good recognition performance is still unclear. To solve these problems, we propose a partitioning graph convolutional network framework for multi-label recognition. First, we segregate the computational graph into multiple sub-graphs. Then, we perform batch normalization operation on each output layer, which can further improve the recognition performance of the network. Finally, extensive experiments are carried out on a multi-label PPT dataset, showing that our proposed solution can greatly improve the feature information utilization of labels and improve the recognition performance.