  We describe a method for visual question answering which is capable of
reasoning about contents of an image on the basis of information extracted from
a large-scale knowledge base. The method not only answers natural language
questions using concepts not contained in the image, but can provide an
explanation of the reasoning by which it developed its answer. The method is
capable of answering far more complex questions than the predominant long
short-term memory-based approach, and outperforms it significantly in the
testing. We also provide a dataset and a protocol by which to evaluate such
methods, thus addressing one of the key issues in general visual ques- tion
answering.
