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

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Raffaella Bernardi
Ruket Cakici
Desmond Elliott
Aykut Erdem
Erkut Erdem
Nazli Ikizler-Cinbis
Frank Keller
Adrian Muscat
Barbara Plank


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.

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