  Visual question answering is concerned with answering free-form questions
about an image. Since it requires a deep linguistic understanding of the
question and the ability to associate it with various objects that are present
in the image, it is an ambitious task and requires techniques from both
computer vision and natural language processing. We propose a novel method that
approaches the task by performing context-driven, sequential reasoning based on
the objects and their semantic and spatial relationships present in the scene.
As a first step, we derive a scene graph which describes the objects in the
image, as well as their attributes and their mutual relationships. A
reinforcement agent then learns to autonomously navigate over the extracted
scene graph to generate paths, which are then the basis for deriving answers.
We conduct a first experimental study on the challenging GQA dataset with
manually curated scene graphs, where our method almost reaches the level of
human performance.
