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

OC-NMN: Object-centric Compositional Neural Module Network for  Generative Visual Analogical Reasoning

Despite the rising prevalence of neural sequence models, recent empirical
evidences suggest their deficiency in compositional generalization. One of the
current de-facto solutions to this problem is compositional data augmentation,
aiming to incur additional compositional inductive bias. Nonetheless, the
improvement offered by existing handcrafted augmentation strategies is limited
when successful systematic generalization of neural sequence models requires
multi-grained compositional bias (i.e., not limited to either lexical or
structural biases only) or differentiation of training sequences in an
imbalanced difficulty distribution. To address the two challenges, we first
propose a novel compositional augmentation strategy dubbed \textbf{Span}
\textbf{Sub}stitution (SpanSub) that enables multi-grained composition of
substantial substructures in the whole training set. Over and above that, we
introduce the \textbf{L}earning \textbf{to} \textbf{S}ubstitute \textbf{S}pan
(L2S2) framework which empowers the learning of span substitution probabilities
in SpanSub in an end-to-end manner by maximizing the loss of neural sequence
models, so as to outweigh those challenging compositions with elusive concepts
and novel surroundings. Our empirical results on three standard compositional
generalization benchmarks, including SCAN, COGS and GeoQuery (with an
improvement of at most 66.5\%, 10.3\%, 1.2\%, respectively), demonstrate the
superiority of SpanSub, %the learning framework L2S2 and their combination.

提出新的跨度替换策略（SpanSub）和基于嵌入式的学习框架（L2S2），分别解决了神经序列模型中的组合泛化问题和不均等难度分布下的挑战性组合问题，成功提高复杂数据集上的性能。