  Answering semantically-complicated questions according to an image is
challenging in Visual Question Answering (VQA) task. Although the image can be
well represented by deep learning, the question is always simply embedded and
cannot well indicate its meaning. Besides, the visual and textual features have
a gap for different modalities, it is difficult to align and utilize the
cross-modality information. In this paper, we focus on these two problems and
propose a Graph Matching Attention (GMA) network. Firstly, it not only builds
graph for the image, but also constructs graph for the question in terms of
both syntactic and embedding information. Next, we explore the intra-modality
relationships by a dual-stage graph encoder and then present a bilateral
cross-modality graph matching attention to infer the relationships between the
image and the question. The updated cross-modality features are then sent into
the answer prediction module for final answer prediction. Experiments
demonstrate that our network achieves state-of-the-art performance on the GQA
dataset and the VQA 2.0 dataset. The ablation studies verify the effectiveness
of each modules in our GMA network.
