Object-centric Compositional Imagination for Visual Abstract Reasoning

Abstract

Like humans devoid of imagination, current machine learning systems lack the ability to adapt to new, unexpected situations by foreseeing them, which makes them unable to solve new tasks by analogical reasoning. In this work, we introduce a new compositional imagination framework that improves a model’s ability to generalize out-of-distribution. One of the key components of our framework is object-centric inductive biases that enables models to perceive the environment as a series of objects, properties, and transformations. By composing these key ingredients, it is possible to generate new unseen tasks that, when used to train the model, improve systematic generalization. Experiments on a simplified version of the \emph{Abstraction and Reasoning Corpus (ARC)} demonstrate the effectiveness of our framework.