  Visual question answering (VQA) usesimage processing algorithms to process
the image and natural language processing methods to understand and answer the
question. VQA is helpful to a visually impaired person, can be used for the
security surveillance system and online chatbots that learn from the web. It
uses NLP methods to learn the semantic of the question and to derive the
textual features. Computer vision techniques are used for generating image
representation in such a way that they can identify the objects about which
question is asked. The Attention model tries to mimic the human behavior of
giving attention to a different region of an image according to our
understanding of its context. This paper critically examines and reviews
methods of VQA algorithm such as generation of semantics of text,
identification of objects and answer classification techniques that use the
co-attention approach.
