Adapted GooLeNet for Visual Question Answering

被引：2
|
作者：
Huang, Jie [1 ]
Hu, Yue [1 ]
Yang, Weilong [1 ]
机构：
[1] Natl Univ Def Technol, Coll Syst Engn, Changsha, Hunan, Peoples R China
来源：
2018 3RD INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE) | 2018年
基金：
中国国家自然科学基金;
关键词：
visual question answering; Adapted GooLeNet; MUTAN;
D O I：
10.1109/ICMCCE.2018.00132
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Visual Question Answering (VQA) aims at answering a question about an image. In this work, we introduce an effective architecture --Adapted GooLeNet (AG)-- into a typical VQA method MUTAN instead of LSTM for question features capturing. This improvement can capture more levels of language granularities in parallel, because of the various sizes of filters in AG. The empirical study on the benchmark dataset of VQA demonstrates that capturing sentence features on different levels of granularities benefit sentence modelling by utilizing AG.
引用
收藏
页码：603 / 606
页数：4
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