  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.
