  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.
