期刊
出版社
IEEE
DOI: 10.1109/ICMCCE.2018.00132
关键词
visual question answering; Adapted GooLeNet; MUTAN
资金
- National Natural Science Foundation of China [61473300]
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.
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