Deep Image Understanding Using Multilayered Contexts

Generation of scene graphs and natural language captions from images for deep image understanding is an ongoing research problem. Scene graphs and natural language captions have a common characteristic in that they are generated by considering the objects in the images and the relationships between the objects. This study proposes a deep neural network model named the Context-based Captioning and Scene Graph Generation Network (C2SGNet), which simultaneously generates scene graphs and natural language captions from images. The proposed model generates results through communication of context information between these two tasks. For effective communication of context information, the two tasks are structured into three layers: the object detection, relationship detection, and caption generation layers. Each layer receives related context information from the lower layer. In this study, the proposed model was experimentally assessed using the Visual Genome benchmark data set. The performance improvement effect of the context information was verified through various experiments. Further, the high performance of the proposed model was confirmed through performance comparison with existing models.