  Recently, the Visual Question Answering (VQA) task has gained increasing
attention in artificial intelligence. Existing VQA methods mainly adopt the
visual attention mechanism to associate the input question with corresponding
image regions for effective question answering. The free-form region based and
the detection-based visual attention mechanisms are mostly investigated, with
the former ones attending free-form image regions and the latter ones attending
pre-specified detection-box regions. We argue that the two attention mechanisms
are able to provide complementary information and should be effectively
integrated to better solve the VQA problem. In this paper, we propose a novel
deep neural network for VQA that integrates both attention mechanisms. Our
proposed framework effectively fuses features from free-form image regions,
detection boxes, and question representations via a multi-modal multiplicative
feature embedding scheme to jointly attend question-related free-form image
regions and detection boxes for more accurate question answering. The proposed
method is extensively evaluated on two publicly available datasets, COCO-QA and
VQA, and outperforms state-of-the-art approaches. Source code is available at
https://github.com/lupantech/dual-mfa-vqa.
