Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures


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Bernardi R., Cakici R., Elliott D., Erdem A., Erdem E., İKİZLER CİNBİŞ N., ...More

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, vol.55, pp.409-442, 2016 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 55
  • Publication Date: 2016
  • Doi Number: 10.1613/jair.4900
  • Journal Name: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.409-442

Abstract

Automatic description generation from natural images is a challenging problem that has recently received a large amount of interest from the computer vision and natural language processing communities. In this survey, we classify the existing approaches based on how they conceptualize this problem, viz., models that cast description as either generation problem or as a retrieval problem over a visual or multimodal representational space. We provide a detailed review of existing models, highlighting their advantages and disadvantages. Moreover, we give an overview of the benchmark image datasets and the evaluation measures that have been developed to assess the quality of machine-generated image descriptions. Finally we extrapolate future directions in the area of automatic image description generation.