A Survey of Current Datasets for Vision and Language Research
This is an incremental survey that helps researchers in AI and vision-language integration by providing tools to assess dataset quality.
The paper tackles the problem of evaluating vision and language datasets by proposing quality metrics and categorizing them, finding that recent datasets use more complex language and abstract concepts but have varied strengths and weaknesses.
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.