Two-Step Joint Attention Network for Visual Question Answering

2017 ◽  
Author(s):  
Weiming Zhang ◽  
Chunhong Zhang ◽  
Pei Liu ◽  
Zhiqiang Zhan ◽  
Xiaofeng Qiu
Keyword(s):  
Joint Attention ◽  
Question Answering ◽  
Attention Network ◽  
Visual Question Answering

scholarly journals Multi-Modality Global Fusion Attention Network for Visual Question Answering

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1882
Author(s):  
Cheng Yang ◽  
Weijia Wu ◽  
Yuxing Wang ◽  
Hong Zhou
Keyword(s):  
Correct Answer ◽  
Question Answering ◽  
Human Beings ◽  
Single Model ◽  
Attention Network ◽  
Attention Model ◽  
Global Perspectives ◽  
Visual Question Answering ◽  
Previous State ◽  
High Level

Visual question answering (VQA) requires a high-level understanding of both questions and images, along with visual reasoning to predict the correct answer. Therefore, it is important to design an effective attention model to associate key regions in an image with key words in a question. Up to now, most attention-based approaches only model the relationships between individual regions in an image and words in a question. It is not enough to predict the correct answer for VQA, as human beings always think in terms of global information, not only local information. In this paper, we propose a novel multi-modality global fusion attention network (MGFAN) consisting of stacked global fusion attention (GFA) blocks, which can capture information from global perspectives. Our proposed method computes co-attention and self-attention at the same time, rather than computing them individually. We validate our proposed method on the two most commonly used benchmarks, the VQA-v2 datasets. Experimental results show that the proposed method outperforms the previous state-of-the-art. Our best single model achieves 70.67% accuracy on the test-dev set of VQA-v2.

scholarly journals Co-Attention Network With Question Type for Visual Question Answering

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 40771-40781 ◽  
Author(s):  
Chao Yang ◽  
Mengqi Jiang ◽  
Bin Jiang ◽  
Weixin Zhou ◽  
Keqin Li
Keyword(s):  
Question Answering ◽  
Question Type ◽  
Attention Network ◽  
Visual Question Answering

Word-to-region attention network for visual question answering

2018 ◽  
Vol 78 (3) ◽  
pp. 3843-3858 ◽  
Author(s):  
Liang Peng ◽  
Yang Yang ◽  
Yi Bin ◽  
Ning Xie ◽  
Fumin Shen ◽  
...  
Keyword(s):  
Question Answering ◽  
Attention Network ◽  
Visual Question Answering

Dynamic Co-attention Network for Visual Question Answering

2021 ◽  
Author(s):  
Doaa B. Ebaid ◽  
Magda M. Madbouly ◽  
Adel A. El-Zoghabi
Keyword(s):  
Question Answering ◽  
Attention Network ◽  
Visual Question Answering

Triple attention network for sentimental visual question answering

2019 ◽  
Vol 189 ◽  
pp. 102829
Author(s):  
Nelson Ruwa ◽  
Qirong Mao ◽  
Heping Song ◽  
Hongjie Jia ◽  
Ming Dong
Keyword(s):  
Question Answering ◽  
Attention Network ◽  
Visual Question Answering
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