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We are specifically able to answer questions posed in natural language, that refer to information not contained in the image. Priming a recurrent neural network with this combined information, and the submitted question, leads to a very flexible visual question answering approach. 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. It particularly allows questions to be asked about the contents of an image, even when the image itself does not contain the whole answer.

This allows more complex questions to be answered using the predominant neural network-based approach than has previously been possible. 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.

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