[3D5-OS-22b] OS-22 (2)

[3D5-OS-22b-01] Corresponding identification between comic images and dialog using distributed representation

〇Akira Terauchi¹, Naoki Mori¹, Miki Ueno² (1. Osaka Prefecture University, 2. Osaka Institute of Technology)

Keywords:Comic Engineering, Multi-modal analysis, Convolutional AutoEncoder

The research of understanding human creations such as comics, novels, and music by artificial intelligence (AI) has become an attractive research topic in AI fields. However, creating an interesting story or comic is still one of the difficult tasks because it requires lots of human creativity. In this study, we focus on that AI can understand comics or not by using four-scene comics because four-scene comics have a clear structure and format. Lots of studies using the image or natural language models have been proposed in such tasks. However, there are few studies using a combination of images and natural language features as multi-modal data. In this study, we proposed the method of combining images and languages to understand four-scene comics utilizing deep learning. The effectiveness of the proposed method is confirmed by computer simulations taking koma prediction problems of four-scene comics as examples.