论文标题
带有多重图网络的抽象图解推理
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
论文作者
论文摘要
抽象推理，尤其是在视觉领域，是一种复杂的人类能力，但对于人工神经学习系统来说仍然是一个具有挑战性的问题。在这项工作中，我们提出了MXGNET，MXGNET是一种多层图神经网络，用于多板图示图推理任务。 MXGNET结合了三个强大的概念，即对象级表示，图形神经网络和多重图，用于求解视觉推理任务。 MXGNET首先在图表的所有面板中提取每个元素的对象级表示，然后形成一个多层多路复用图，该图捕获了不同图表面板之间的对象之间的多个关系。 MXGNET总结了从任务图中提取的多个图，并使用此摘要来从给定的候选人中选择最可能的答案。我们已经在两种类型的示意力推理任务上测试了MXGNET，即图表三段论和Raven Prograckive矩阵（RPM）。对于Euler图，三段论任务MXGNET的最新精度为99.8％。对于PGM和Raven，两个用于RPM推理的综合数据集，MXGNET的表现优于最先进的模型。
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.
