scholarly journals A cascaded long short-term memory (LSTM) driven generic visual question answering (VQA)

Author(s):  
Iqbal Chowdhury ◽  
Kien Nguyen ◽  
Clinton Fookes ◽  
Sridha Sridharan
Author(s):  
Peng Wang ◽  
Qi Wu ◽  
Chunhua Shen ◽  
Anthony Dick ◽  
Anton van den Hengel

We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts not contained in the image, but can explain the reasoning by which it developed its answer. It is capable of answering far more complex questions than the predominant long short-term memory-based approach, and outperforms it significantly in testing. We also provide a dataset and a protocol by which to evaluate general visual question answering methods.


2021 ◽  
Author(s):  
Seyed Vahid Moravvej ◽  
Mohammad Javad Maleki Kahaki ◽  
Moein Salimi Sartakhti ◽  
Abdolreza Mirzaei

Author(s):  
Weijie Yang ◽  
Hong Ma

In this paper, for the Chinese automatic question answering technology in open domain, in addition to considering the traditional association between questions and questions, the correlation between questions and answers is added. The cosine similarity between questions and answers is used as the semantic similarity between them. A bi-directional long short-term memory network (BiLSTM) is added between the question and question, answer and the answer to seek the association between the contexts. and an attention mechanism is added to make question and answer related. Finally, the experimental verification shows that the accuracy of automatic question answering by the proposed method reaches 70%.


2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi

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