Adaptive attention fusion network for visual question answering
Geonmo Gu, Seong Tae Kim, Yong Man Ro · 2017
Automatic understanding of the content of a reference image and natural language questions is needed in Visual Question Answering (VQA). Generating a visual attention map that focuses on the regions related to the context of the question can improve performance of VQA. In this paper, we propose adaptive attention-based VQA network. The proposed method utilizes the complementary information from the attention maps depending on three levels of word embedding (word level, phrase level, and question level embedding), and adaptively fuses the information to represent the image-question pair appropriately. Comparative experiments have been conducted on the public COCO-QA database to validate the proposed method. Experimental results have shown that the proposed method outperforms previous methods in terms of accuracy.