  Visual Question Answering (VQA) is an extremely stimulating and challenging
research area where Computer Vision (CV) and Natural Language Processig (NLP)
have recently met. In image captioning and video summarization, the semantic
information is completely contained in still images or video dynamics, and it
has only to be mined and expressed in a human-consistent way. Differently from
this, in VQA semantic information in the same media must be compared with the
semantics implied by a question expressed in natural language, doubling the
artificial intelligence-related effort. Some recent surveys about VQA
approaches have focused on methods underlying either the image-related
processing or the verbal-related one, or on the way to consistently fuse the
conveyed information. Possible applications are only suggested, and, in fact,
most cited works rely on general-purpose datasets that are used to assess the
building blocks of a VQA system. This paper rather considers the proposals that
focus on real-world applications, possibly using as benchmarks suitable data
bound to the application domain. The paper also reports about some recent
challenges in VQA research.
