scholarly journals Knowing Where to Look? Analysis on Attention of Visual Question Answering System

2019 ◽  
pp. 145-152 ◽  
Author(s):  
Wei Li ◽  
Zehuan Yuan ◽  
Xiangzhong Fang ◽  
Changhu Wang
Keyword(s):  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering

A new visual question answering system for medical images characterization

2019 ◽  
Author(s):  
Bghiel Afrae ◽  
Dahdouh Yousra ◽  
Allaouzi Imane ◽  
Ben Ahmed Mohamed ◽  
Anouar Boudhir Abdelhakim
Keyword(s):  
Question Answering ◽  
Medical Images ◽  
Question Answering System ◽  
Visual Question Answering

scholarly journals Predicting drug–protein interaction using quasi-visual question answering system

2020 ◽  
Vol 2 (2) ◽  
pp. 134-140 ◽  
Author(s):  
Shuangjia Zheng ◽  
Yongjian Li ◽  
Sheng Chen ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Protein Interaction ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering

scholarly journals Publisher Correction: Predicting drug–protein interaction using quasi-visual question answering system

2020 ◽  
Vol 2 (9) ◽  
pp. 551-551
Author(s):  
Shuangjia Zheng ◽  
Yongjian Li ◽  
Sheng Chen ◽  
Jun Xu ◽  
Yuedong Yang
Keyword(s):  
Protein Interaction ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering

Visual Question-Answering System Using Integrated Models of Image Captioning and BERT

2021 ◽  
pp. 169-190
Author(s):  
Lavika Goel ◽  
Mohit Dhawan ◽  
Rachit Rathore ◽  
Satyansh Rai ◽  
Aaryan Kapoor ◽  
...  
Keyword(s):  
Question Answering ◽  
Integrated Models ◽  
Image Captioning ◽  
Question Answering System ◽  
Visual Question Answering

scholarly journals Visual Question Answering using Convolutional Neural Networks

2021 ◽  
Vol 12 (1S) ◽  
pp. 170-175
Author(s):  
K. P. Moholkar, Et. al.
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Question Answering System ◽  
Visual Question Answering ◽  
Generalized System ◽  
Multiple Scenarios

The ability of a computer system to be able to understand surroundings and elements and to think like a human being to process the information has always been the major point of focus in the field of Computer Science. One of the ways to achieve this artificial intelligence is Visual Question Answering. Visual Question Answering (VQA) is a trained system which can answer the questions associated to a given image in Natural Language. VQA is a generalized system which can be used in any image-based scenario with adequate training on the relevant data. This is achieved with the help of Neural Networks, particularly Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In this study, we have compared different approaches of VQA, out of which we are exploring CNN based model. With the continued progress in the field of Computer Vision and Question answering system, Visual Question Answering is becoming the essential system which can handle multiple scenarios with their respective data.

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