  Visual question answering (or VQA) is a new and exciting problem that
combines natural language processing and computer vision techniques. We present
a survey of the various datasets and models that have been used to tackle this
task. The first part of the survey details the various datasets for VQA and
compares them along some common factors. The second part of this survey details
the different approaches for VQA, classified into four types: non-deep learning
models, deep learning models without attention, deep learning models with
attention, and other models which do not fit into the first three. Finally, we
compare the performances of these approaches and provide some directions for
future work.
