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Visual Question Answering

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dc.contributor.author Borana, Tarun
dc.date.accessioned 2023-07-14T16:42:14Z
dc.date.available 2023-07-14T16:42:14Z
dc.date.issued 2022-07
dc.identifier.citation 28p. en_US
dc.identifier.uri http://hdl.handle.net/10263/7382
dc.description Dissertation under the supervision of Dr. Ujjwal Bhattacharya en_US
dc.description.abstract In recent years, tremendous progress has been made in the fields of object detection, computer vision, and natural language processing. Artificial intelligence Systems (AI), such as question-answering models provide the machine with "comprehensive" capabilities using natural language processing. Such a machine can respond to queries in natural language about an unstructured text. For performing the task of VQA, we can combine Natural language processing with computer vision.The purpose of a visual question answering system is to create a system capable of answering natural language queries about images. A number of systems have been introduced for visual question answering that use learning algorithms and deep-learning architectures. This project introduces a VQA system that uses deep understanding of images using a deep convolutional neural network (CNN) that helps to extract features from image and LSTM are used for word embeddings for question texts.in this project we are taking only those questions that have answer type yes or no. Hence, Our system achieves complex reasoning and natural language understanding so that it can correctly predict the request and give the appropriate answer yes or no. Different architectures are introduced to combine the image and language models. en_US
dc.language.iso en en_US
dc.publisher Indian Statistical Institute, Kolkata en_US
dc.relation.ispartofseries Dissertation;2022-7
dc.subject Long short-termmemory en_US
dc.subject Neural Network Architectures en_US
dc.title Visual Question Answering en_US
dc.type Other en_US


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