  Visual Question Answering (VQA) is a novel problem domain where multi-modal
inputs must be processed in order to solve the task given in the form of a
natural language. As the solutions inherently require to combine visual and
natural language processing with abstract reasoning, the problem is considered
as AI-complete. Recent advances indicate that using high-level, abstract facts
extracted from the inputs might facilitate reasoning. Following that direction
we decided to develop a solution combining state-of-the-art object detection
and reasoning modules. The results, achieved on the well-balanced CLEVR
dataset, confirm the promises and show significant, few percent improvements of
accuracy on the complex "counting" task.
