| SPECIAL SECTION: COMPUTATIONAL LINGUISTICS |
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Exploiting image captions and external knowledge as representation enhancement for VQA |
| WANG Yichao, ZHU Muhua, XU Chen, ZHANG Yan, WANG Huizhen, ZHU Jingbo |
| Natural Language Processing Lab, School of Computer Science and Engineering, Northeastern University, Shenyang 110000, China |
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Abstract As a multimodal task, visual question answering (VQA) requires a comprehensive understanding of images and questions. However, conducting reasoning simply on images and questions may fail in some cases. Other information that can be used for the task, such as image captions and external knowledge base, exists. A novel approach is proposed in this paper to incorporate information on image captions and external knowledge into VQA models. The proposed approach adopts the co-attention mechanism and encodes image captions with the guidance from the question to utilize image captions. Moreover, the approach incorporates external knowledge by using knowledge graph embedding as the initialization of word embeddings. The above methods enrich the capability of feature representation and model reasoning. Experimental results on the OKVQA dataset show that the proposed method achieves an improvement of 1.71% and 1.88% over the baseline and best-reported previous systems, respectively, which proved the effectiveness of this method.
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| Keywords
visual question answering
multimodal fusion
knowledge graph
image captioning
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Issue Date: 26 April 2022
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2022,
Vol. 62
