Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures
January 15, 2016 ยท The Cartographer ยท ๐ Journal of Artificial Intelligence Research
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"Title-pattern auto-detect: Automatic Description Generation from Images: A Survey of Models, Datasets, and Evaluation Measures"
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Authors
Raffaella Bernardi, Ruket Cakici, Desmond Elliott, Aykut Erdem, Erkut Erdem, Nazli Ikizler-Cinbis, Frank Keller, Adrian Muscat, Barbara Plank
arXiv ID
1601.03896
Category
cs.CL: Computation & Language
Cross-listed
cs.CV
Citations
379
Venue
Journal of Artificial Intelligence Research
Last Checked
1 day ago
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.
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