A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such as Raven's Progressive Matrices (RPM). Motivated by the goal of designing AI systems with this capacity, recent work has focused on evaluating whether neural networks can learn to solve RPM-like problems. Previous work has generally found that strong performance on these problems requires the incorporation of inductive biases that are specific to the RPM problem format, raising the question of whether such models might be more broadly useful. Here, we investigated the extent to which a general-purpose mechanism for processing visual scenes in terms of objects might help promote abstract visual reasoning. We found that a simple model, consisting only of an object-centric encoder and a transformer reasoning module, achieved state-of-the-art results on both of two challenging RPM-like benchmarks (PGM and I-RAVEN), as well as a novel benchmark with greater visual complexity (CLEVR-Matrices). These results suggest that an inductive bias for object-centric processing may be a key component of abstract visual reasoning, obviating the need for problem-specific inductive biases.

翻译：人类智能的一个核心组成部分是识别复杂高维感知数据中固有的抽象模式的能力，例如瑞文推理测验（RPM）等视觉推理任务。为了设计具备这种能力的AI系统，近期研究聚焦于评估神经网络是否能学习解决类RPM问题。以往研究普遍发现，在这些问题上取得优秀性能需要融入针对RPM问题格式的归纳偏置，这引发了此类模型是否具有更广泛适用性的疑问。本文探讨了基于对象处理视觉场景的通用机制在多大程度上能促进抽象视觉推理。我们发现，一个仅由对象中心编码器和Transformer推理模块组成的简单模型，在两个具有挑战性的类RPM基准（PGM和I-RAVEN）以及一个视觉复杂性更高的新基准（CLEVR-Matrices）上均达到了当前最优结果。这些结果表明，面向对象处理的归纳偏置可能是抽象视觉推理的关键要素，能够替代问题专用的归纳偏置。