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WMA: A Multi-Scale Self-Attention Feature Extraction Network Based on Weight Sharing for VQA

Yue Li, Jin Liu*, Shengjie Shang

Shanghai Maritime University, Shanghai, 201306, China

* Corresponding Author: Jin Liu. Email: email

Journal on Big Data 2021, 3(3), 111-118. https://doi.org/10.32604/jbd.2021.017169

Abstract

Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above problem is designed. Our approach achieves the state-of-the-art performance of a single model on Pascal VOC 2012, VQA 1.0, and VQA 2.0 datasets.

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APA Style
Li, Y., Liu, J., Shang, S. (2021). WMA: A multi-scale self-attention feature extraction network based on weight sharing for VQA. Journal on Big Data, 3(3), 111-118. https://doi.org/10.32604/jbd.2021.017169
Vancouver Style
Li Y, Liu J, Shang S. WMA: A multi-scale self-attention feature extraction network based on weight sharing for VQA. J Big Data . 2021;3(3):111-118 https://doi.org/10.32604/jbd.2021.017169
IEEE Style
Y. Li, J. Liu, and S. Shang "WMA: A Multi-Scale Self-Attention Feature Extraction Network Based on Weight Sharing for VQA," J. Big Data , vol. 3, no. 3, pp. 111-118. 2021. https://doi.org/10.32604/jbd.2021.017169



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