A key aspect of human intelligence is the ability to imagine -- composing learned concepts in novel ways -- to make sense of new scenarios. Such capacity is not yet attained for machine learning systems. In this work, in the context of visual reasoning, we show how modularity can be leveraged to derive a compositional data augmentation framework inspired by imagination. Our method, denoted Object-centric Compositional Neural Module Network (OC-NMN), decomposes visual generative reasoning tasks into a series of primitives applied to objects without using a domain-specific language. We show that our modular architectural choices can be used to generate new training tasks that lead to better out-of-distribution generalization. We compare our model to existing and new baselines in proposed visual reasoning benchmark that consists of applying arithmetic operations to MNIST digits.

人工智能的一个关键方面是想象力，即以新颖的方式组合学习的概念，以理解新的情境。本研究展示了如何利用模块化和想象力创建一个基于物体的组合神经模块网络（OC-NMN）框架，用于视觉推理和生成任务，并证明该方法可以带来更好的非分布式泛化性能。

OC-NMN: 面向生成性视觉类比推理的物体中心化组合神经模块网络