Abstract Diagrammatic Reasoning with Multiplex Graph Networks

TL;DR

This paper introduces MXGNet, a novel multilayer graph neural network that effectively models multi-panel diagrammatic reasoning by integrating object representations and multiplex graphs, achieving state-of-the-art results on reasoning tasks.

Contribution

MXGNet is the first model to combine object-level features, graph neural networks, and multiplex graphs for visual reasoning tasks, advancing the capabilities of neural systems in abstract diagrammatic reasoning.

Findings

Achieves 99.8% accuracy on Euler Diagram Syllogisms.

Outperforms existing models on PGM and RAVEN datasets.

Demonstrates the effectiveness of multiplex graphs in reasoning tasks.

Abstract

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. MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates. We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM). For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%. For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin.