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

January 15, 2016 ยท The Cartographer ยท ๐Ÿ› Journal of Artificial Intelligence Research

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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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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