  In order to answer semantically-complicated questions about an image, a
Visual Question Answering (VQA) model needs to fully understand the visual
scene in the image, especially the interactive dynamics between different
objects. We propose a Relation-aware Graph Attention Network (ReGAT), which
encodes each image into a graph and models multi-type inter-object relations
via a graph attention mechanism, to learn question-adaptive relation
representations. Two types of visual object relations are explored: (i)
Explicit Relations that represent geometric positions and semantic interactions
between objects; and (ii) Implicit Relations that capture the hidden dynamics
between image regions. Experiments demonstrate that ReGAT outperforms prior
state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further
show that ReGAT is compatible to existing VQA architectures, and can be used as
a generic relation encoder to boost the model performance for VQA.
