  Modeling visual question answering(VQA) through scene graphs can
significantly improve the reasoning accuracy and interpretability. However,
existing models answer poorly for complex reasoning questions with attributes
or relations, which causes false attribute selection or missing relation in
Figure 1(a). It is because these models cannot balance all kinds of information
in scene graphs, neglecting relation and attribute information. In this paper,
we introduce a novel Dual Message-passing enhanced Graph Neural Network
(DM-GNN), which can obtain a balanced representation by properly encoding
multi-scale scene graph information. Specifically, we (i)transform the scene
graph into two graphs with diversified focuses on objects and relations; Then
we design a dual structure to encode them, which increases the weights from
relations (ii)fuse the encoder output with attribute features, which increases
the weights from attributes; (iii)propose a message-passing mechanism to
enhance the information transfer between objects, relations and attributes. We
conduct extensive experiments on datasets including GQA, VG, motif-VG and
achieve new state of the art.
