A Survey of Current Datasets for Vision and Language Research

  • Francis Ferraro
  • Nasrin Mostafazadeh
  • Ting-Hao (Kenneth) Huang
  • Lucy Vanderwende
  • Jacob Devlin

Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP) |

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Integrating vision and language has long been a dream in work on artificial intelligence (AI). In the past two years, we have witnessed an explosion of work that brings together vision and language from images to videos and beyond. The available corpora have played a crucial role in advancing this area of research. In this paper, we propose a set of quality metrics for evaluating and analyzing the vision & language datasets and categorize them accordingly. Our analyses show that the most recent datasets have been using more complex language and more abstract concepts, however, there are different strengths and weaknesses in each.