  The Visual Question Answering (VQA) task combines challenges for processing
data with both Visual and Linguistic processing, to answer basic `common sense'
questions about given images. Given an image and a question in natural
language, the VQA system tries to find the correct answer to it using visual
elements of the image and inference gathered from textual questions. In this
survey, we cover and discuss the recent datasets released in the VQA domain
dealing with various types of question-formats and robustness of the
machine-learning models. Next, we discuss about new deep learning models that
have shown promising results over the VQA datasets. At the end, we present and
discuss some of the results computed by us over the vanilla VQA model, Stacked
Attention Network and the VQA Challenge 2017 winner model. We also provide the
detailed analysis along with the challenges and future research directions.
