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Measuring Machine Intelligence Through Visual Question Answering
lib:a83a72fea4a2143c (v1.0.0)
Authors: C. Lawrence Zitnick,Aishwarya Agrawal,Stanislaw Antol,Margaret Mitchell,Dhruv Batra,Devi Parikh
ArXiv: 1608.08716
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1608.08716v1
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.
ArXiv: 1608.08716
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1608.08716v1
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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