  Images are more than a collection of objects or attributes -- they represent
a web of relationships among interconnected objects. Scene Graph has emerged as
a new modality as a structured graphical representation of images. Scene Graph
encodes objects as nodes connected via pairwise relations as edges. To support
question answering on scene graphs, we propose GraphVQA, a language-guided
graph neural network framework that translates and executes a natural language
question as multiple iterations of message passing among graph nodes. We
explore the design space of GraphVQA framework, and discuss the trade-off of
different design choices. Our experiments on GQA dataset show that GraphVQA
outperforms the state-of-the-art accuracy by a large margin (88.43% vs.
94.78%).
