A Survey of Current Datasets for Vision and Language Research

June 23, 2015 · The Cartographer · 🏛 Conference on Empirical Methods in Natural Language Processing

"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey of Current Datasets for Vision and Language Research"

Evidence collected by the PWNC Scanner

Authors Francis Ferraro, Nasrin Mostafazadeh, Ting-Hao, Huang, Lucy Vanderwende, Jacob Devlin, Michel Galley, Margaret Mitchell arXiv ID 1506.06833 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.CV Citations 77 Venue Conference on Empirical Methods in Natural Language Processing Last Checked 8 days ago

Abstract

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.