한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)

한국정보처리학회 (Korea Information Processing Society)
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딥인코더-디코더 기반의 인공지능 포토 스토리텔러

AI photo storyteller based on deep encoder-decoder architecture

  • 민경복 (세종대학교 컴퓨터 공학과) ;
  • ;
  • 이수진 (중앙대학교 다빈치 SW 교육원) ;
  • 문현준 (세종대학교 컴퓨터 공학과)
  • Min, Kyungbok (Department of Computer Science and Engineering, Sejong University) ;
  • Dang, L. Minh (Department of Computer Science and Engineering, Sejong University) ;
  • Lee, Sujin (Department of Da Vinchi SW Education, Chungang University) ;
  • Moon, Hyeonjoon (Department of Computer Science and Engineering, Sejong University)
  • 발행 : 2019.10.30
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초록

Research using artificial intelligence to generate captions for an image has been studied extensively. However, these systems are unable to create creative stories that include more than one sentence based on image content. A story is a better way that humans use to foster social cooperation and develop social norms. This paper proposes a framework that can generate a relatively short story to describe based on the context of an image. The main contributions of this paper are (1) An unsupervised framework which uses recurrent neural network structure and encoder-decoder model to construct a short story for an image. (2) A huge English novel dataset, including horror and romantic themes that are manually collected and validated. By investigating the short stories, the proposed model proves that it can generate more creative contents compared to existing intelligent systems which can produce only one concise sentence. Therefore, the framework demonstrated in this work will trigger the research of a more robust AI story writer and encourages the application of the proposed model in helping story writer find a new idea.

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과제정보

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation), and by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agri-Bio Industry Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (316033-04-2-338 SB030).