Knowledge Detection by Relevant Question and Image Attributes in Visual
  Question Answering
Param Ahir Dr. Hiteishi Diwanji
Visual question answering (VQA) is a Multidisciplinary research problem that
pursued through practices of natural language processing and computer vision.
Visual question answering automatically answers natural language questions
according to the content of an image. Some testing questions require external
knowledge to derive a solution. Such knowledge-based VQA uses various methods
to retrieve features of image and text, and combine them to generate the
answer. To generate knowledgebased answers either question dependent or image
dependent knowledge retrieval methods are used. If knowledge about all the
objects in the image is derived, then not all knowledge is relevant to the
question. On other side only question related knowledge may lead to incorrect
answers and over trained model that answers question that is irrelevant to
image. Our proposed method takes image attributes and question features as
input for knowledge derivation module and retrieves only question relevant
knowledge about image objects which can provide accurate answers.