  Visual question answering (VQA) is a challenging multi-modal task that
requires not only the semantic understanding of both images and questions, but
also the sound perception of a step-by-step reasoning process that would lead
to the correct answer. So far, most successful attempts in VQA have been
focused on only one aspect, either the interaction of visual pixel features of
images and word features of questions, or the reasoning process of answering
the question in an image with simple objects. In this paper, we propose a deep
reasoning VQA model with explicit visual structure-aware textual information,
and it works well in capturing step-by-step reasoning process and detecting a
complex object-relationship in photo-realistic images. REXUP network consists
of two branches, image object-oriented and scene graph oriented, which jointly
works with super-diagonal fusion compositional attention network. We
quantitatively and qualitatively evaluate REXUP on the GQA dataset and conduct
extensive ablation studies to explore the reasons behind REXUP's effectiveness.
Our best model significantly outperforms the precious state-of-the-art, which
delivers 92.7% on the validation set and 73.1% on the test-dev set.
