Our world can be succinctly and compactly described as structured scenes of
objects and relations. A typical room, for example, contains salient objects
such as tables, chairs and books, and these objects typically relate to each
other by their underlying causes and semantics. This gives rise to correlated
features, such as position, function and shape. Humans exploit knowledge of
objects and their relations for learning a wide spectrum of tasks, and more
generally when learning the structure underlying observed data. In this work,
we introduce relation networks (RNs) - a general purpose neural network
architecture for object-relation reasoning. We show that RNs are capable of
learning object relations from scene description data. Furthermore, we show
that RNs can act as a bottleneck that induces the factorization of objects from
entangled scene description inputs, and from distributed deep representations
of scene images provided by a variational autoencoder. The model can also be
used in conjunction with differentiable memory mechanisms for implicit relation
discovery in one-shot learning tasks. Our results suggest that relation
networks are a potentially powerful architecture for solving a variety of
problems that require object relation reasoning.

本文引入的关系网络（RNs）- 一种通用的神经网络架构，可用于从场景描述数据中学习对象关系，并从变分自编码器提供的场景图像的分布式深度表示中分离出错综场景描述输入的对象。