基于多路语义图网络的图像自动问答.

Recently, image question answering based on the fusion of visual features and text features has become one of the hot research issues of automatic question answering. Most of the existing models are based on the attention mechanism to explore the relationship between the image and the question sentence, which ignores the correlation between the image area and the question words in the same mode and different views. To solve these problems, this paper proposed an image question answering model (MSGN) based on multi-view semantic graph network, which could mine the semantic correlation between images and questions from multiple views. Meanwhile, it used the graph neural network model to mine the fine-grained intra and inter-modal correlation between image regions and question words. It carried out extensive experiments on public data sets. The experimental results show that the image automatic question answering model based on multi-view semantic graph network can improve the performance of image question answering. [ABSTRACT FROM AUTHOR]