Visual question answering model based on graph neural network and contextual attention

2021 ◽  
pp. 104165
Author(s):  
Himanshu Sharma ◽  
Anand Singh Jalal
Keyword(s):  
Neural Network ◽  
Question Answering ◽  
Visual Question Answering ◽  
Model Based

Visual question answering model based on visual relationship detection

2020 ◽  
Vol 80 ◽  
pp. 115648 ◽  
Author(s):  
Yuling Xi ◽  
Yanning Zhang ◽  
Songtao Ding ◽  
Shaohua Wan
Keyword(s):  
Question Answering ◽  
Visual Question Answering ◽  
Model Based

scholarly journals Analysis of English Multitext Reading Comprehension Model Based on Deep Belief Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiaohui Tang
Keyword(s):  
Neural Network ◽  
Reading Comprehension ◽  
Question Answering ◽  
Test Analysis ◽  
Model Based ◽  
English Reading ◽  
Comprehension Ability ◽  
Low Efficiency ◽  
Machine Reading ◽  
Strong Reading

In order to solve the problems of low accuracy and low efficiency of answer prediction in machine reading comprehension, a multitext English reading comprehension model based on the deep belief neural network is proposed. Firstly, the paragraph selector in the multitext reading comprehension model is constructed. Secondly, the text reader is designed, and the deep belief neural network is introduced to predict the question answering probability. Finally, the popular English dataset of SQuAD is used for test analysis. The final results show that, after the comparative analysis of different learning methods, it is found that the English multitext reading comprehension model has a strong reading comprehension ability. In addition, two evaluation methods are used to score the overall performance of the model, which shows that the overall score of the English multitext reading comprehension model based on the deep confidence neural network is more than 90, and the efficiency will not be reduced because of the change of the number of documents in the dataset. The above results show that the use of the deep belief neural network to improve the probability generation performance of the model can well solve the task of English multitext reading comprehension, effectively reduce the difficulty of machine reading comprehension in multitask reading, and has a good guiding significance for promoting human convenient Internet knowledge acquisition.

scholarly journals Joint embedding VQA model based on dynamic word vector

2021 ◽  
Vol 7 ◽  
pp. e353
Author(s):  
Zhiyang Ma ◽  
Wenfeng Zheng ◽  
Xiaobing Chen ◽  
Lirong Yin
Keyword(s):  
Question Answering ◽  
Feature Fusion ◽  
Image Feature ◽  
Text And Image ◽  
Visual Question Answering ◽  
Model Based ◽  
Joint Embedding ◽  
Real Language ◽  
Language Environment ◽  
Better Than

The existing joint embedding Visual Question Answering models use different combinations of image characterization, text characterization and feature fusion method, but all the existing models use static word vectors for text characterization. However, in the real language environment, the same word may represent different meanings in different contexts, and may also be used as different grammatical components. These differences cannot be effectively expressed by static word vectors, so there may be semantic and grammatical deviations. In order to solve this problem, our article constructs a joint embedding model based on dynamic word vector—none KB-Specific network (N-KBSN) model which is different from commonly used Visual Question Answering models based on static word vectors. The N-KBSN model consists of three main parts: question text and image feature extraction module, self attention and guided attention module, feature fusion and classifier module. Among them, the key parts of N-KBSN model are: image characterization based on Faster R-CNN, text characterization based on ELMo and feature enhancement based on multi-head attention mechanism. The experimental results show that the N-KBSN constructed in our experiment is better than the other 2017—winner (glove) model and 2019—winner (glove) model. The introduction of dynamic word vector improves the accuracy of the overall results.

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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