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：人类智能的核心组成部分之一，是从复杂、高维感知数据中识别抽象模式的能力，例如雷文渐进矩阵（RPM）等视觉推理任务。受设计具备该能力的人工智能系统目标的驱动，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异性能需引入针对RPM问题格式的归纳偏置，这引发了对该类模型是否具有更广泛适用性的疑问。本研究探讨了以对象为中心的通用视觉场景处理机制在何种程度上能促进抽象视觉推理。结果表明，仅由对象中心编码器与Transformer推理模块构成的简单模型，在两个具有挑战性的类RPM基准测试（PGM和I-RAVEN）以及一个视觉复杂度更高的新型基准测试（CLEVR-Matrices）中均取得了最先进的结果。这些发现表明，以对象为中心的归纳偏置可能是抽象视觉推理的关键要素，可消除对问题特定归纳偏置的需求。