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.

翻译：视觉问答（VQA）是一个结合自然语言处理与计算机视觉技术的多学科研究问题，旨在根据图像内容自动回答自然语言问题。部分测试问题需要借助外部知识才能求解。这类基于知识的VQA采用多种方法提取图像与文本特征，并将其融合生成答案。为生成基于知识的答案，通常采用问题依赖或图像依赖的知识检索方法。若提取图像中所有物体的知识，则部分知识可能与问题无关；反之，仅依赖问题相关知识可能导致错误答案及模型过训练，从而回答与图像内容无关的问题。本文提出的方法将图像属性与问题特征作为知识推导模块的输入，仅检索与问题相关的图像物体知识，从而提供准确答案。