Abstract Diagrammatic Reasoning with Multiplex Graph Networks

Understand

Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems.

• In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks.
• MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks.
• MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels.