Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering
This addresses the challenge of improving answer accuracy in VQA by reducing irrelevant knowledge, which is an incremental advancement for the VQA research community.
The paper tackles the problem of irrelevant or excessive knowledge retrieval in knowledge-based visual question answering (VQA), proposing a method that uses image attributes and question features to retrieve only question-relevant knowledge about image objects, resulting in more accurate answers.
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.