Visual Question Answering Using Deep Learning

Visual Question Answering Using Deep Learning

Mrs.Pallavi N R1, Anusha A R2, Chethan Kumar S3, Hemanth G K4, Reetha D5

1 Mrs.Pallavi N R, Asst. Professor, BGS Institute of Technology

2Anusha A R, Department of Computer Science and Engineering, BGS Institute of Technology 3Chethan Kumar S, Department of Computer Science and Engineering, BGS Institute of Technology 4Hemanth G K, Department of Computer Science and Engineering, BGS Institute of Technology , 5Reetha D, Department of Computer Science and Engineering, BGS Institute of Technology

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Abstract – Answers to Practical Questions is a research center on how to build a computer program to answer questions asked in a clear and natural language. First, let us consider three key elements to answering the accompanying questions. Answering visual questions is a proposed task of integrating computer imaging and natural language processing (NLP), to facilitate research, and to push the boundaries of both fields. Computer programs, on the other hand, learn to detect, process, and process images. In short, their purpose is to teach the machines to see. NLP, on the other hand, is a field that allows for interaction between computers and people in a natural language, i.e. learning materials, among other activities. Both computer theory and NLP are the domain of artificial intelligence and share similar methods based on machine learning. Historically, however, they have developed separately. An effective way is to integrate convolutional neural networks (CNNs), which are trained in object recognition, and embedded words, trained in corporate writing.

Key Words: Natural Language Processing, Computer Vision, Convolutional Neural Network.