  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.

Computer Vision and Pattern Recognition;Computation and Language