  We propose a method for visual question answering which combines an internal
representation of the content of an image with information extracted from a
general knowledge base to answer a broad range of image-based questions. This
allows more complex questions to be answered using the predominant neural
network-based approach than has previously been possible. It particularly
allows questions to be asked about the contents of an image, even when the
image itself does not contain the whole answer. The method constructs a textual
representation of the semantic content of an image, and merges it with textual
information sourced from a knowledge base, to develop a deeper understanding of
the scene viewed. Priming a recurrent neural network with this combined
information, and the submitted question, leads to a very flexible visual
question answering approach. We are specifically able to answer questions posed
in natural language, that refer to information not contained in the image. We
demonstrate the effectiveness of our model on two publicly available datasets,
Toronto COCO-QA and MS COCO-VQA and show that it produces the best reported
results in both cases.
