Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to unseen problems. Recent work has demonstrated strong systematic generalization in visual reasoning tasks involving multi-object inputs, through the integration of slot-based methods used for extracting object-centric representations coupled with strong inductive biases for relational abstraction. However, this approach was limited to problems containing a single rule, and was not scalable to visual reasoning problems containing a large number of objects. Other recent work proposed Abstractors, an extension of Transformers that incorporates strong relational inductive biases, thereby inheriting the Transformer's scalability and multi-head architecture, but it has yet to be demonstrated how this approach might be applied to multi-object visual inputs. Here we combine the strengths of the above approaches and propose Slot Abstractors, an approach to abstract visual reasoning that can be scaled to problems involving a large number of objects and multiple relations among them. The approach displays state-of-the-art performance across four abstract visual reasoning tasks.

翻译：抽象视觉推理是人类特有的能力，能够识别脱离物体特征的关系模式，并将这些模式系统性地泛化到未见问题中。近期研究通过整合用于提取以对象为中心的表征的插槽方法，并结合关系抽强的归纳偏置，在涉及多对象输入的视觉推理任务中展现了强大的系统性泛化能力。然而，该方法仅限于包含单一规则的问题，无法扩展到含有大量对象的视觉推理问题。其他近期研究提出了Abstractors，这是Transformer的一种扩展，融入了强关系归纳偏置，从而继承了Transformer的可扩展性和多头架构，但尚未证明该方法如何应用于多对象视觉输入。本文结合上述方法的优势，提出Slot Abstractors——一种可扩展至涉及大量对象及其多重关系的抽象视觉推理方法。该方法在四项抽象视觉推理任务中展现了最先进的性能。