Measuring Machine Intelligence Through Visual Question Answering

Authors

  • C. Lawrence Zitnick Facebook AI Research
  • Aishwarya Agrawal Virginia Institute of Technology
  • Stanislaw Antol Virginia Institute of Technology
  • Margaret Mitchell Microsoft Research
  • Dhruv Batra Virginia Institute of Technology
  • Devi Parikh Virginia Institute of Technology

DOI:

https://doi.org/10.1609/aimag.v37i1.2647

Abstract

As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine’s ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

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Published

2016-04-13

How to Cite

Zitnick, C. L., Agrawal, A., Antol, S., Mitchell, M., Batra, D., & Parikh, D. (2016). Measuring Machine Intelligence Through Visual Question Answering. AI Magazine, 37(1), 63-72. https://doi.org/10.1609/aimag.v37i1.2647

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Section

Articles