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, as well as an abstract reasoning task involving real-world images.

翻译：抽象视觉推理是人类特有的能力，它允许识别从物体特征中抽象出来的关系模式，并将这些模式系统性地推广到未见问题中。近期研究通过整合用于提取以物体为中心表示的槽位方法与关系抽象的强归纳偏置，在涉及多物体输入的视觉推理任务中展示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，且无法扩展到包含大量物体的视觉推理问题。另一项最新研究提出了Abstractors——一种融入强关系归纳偏置的Transformer扩展模型，从而继承了Transformer的可扩展性与多头架构，但该方法如何应用于多物体视觉输入尚未得到验证。本文综合上述方法的优势，提出槽位抽象器——一种能够扩展到涉及大量物体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以及一项涉及真实世界图像的抽象推理任务中均表现出最先进的性能。