Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative
  Cognition Approach
Guillermo Puebla Jeffrey S. Bowers
Achieving visual reasoning is a long-term goal of artificial intelligence. In
the last decade, several studies have applied deep neural networks (DNNs) to
the task of learning visual relations from images, with modest results in terms
of generalization of the relations learned. However, in recent years,
object-centric representation learning has been put forward as a way to achieve
visual reasoning within the deep learning framework. Object-centric models
attempt to model input scenes as compositions of objects and relations between
them. To this end, these models use several kinds of attention mechanisms to
segregate the individual objects in a scene from the background and from other
objects. In this work we tested relation learning and generalization in several
object-centric models, as well as a ResNet-50 baseline. In contrast to previous
research, which has focused heavily in the same-different task in order to
asses relational reasoning in DNNs, we use a set of tasks -- with varying
degrees of difficulty -- derived from the comparative cognition literature. Our
results show that object-centric models are able to segregate the different
objects in a scene, even in many out-of-distribution cases. In our simpler
tasks, this improves their capacity to learn and generalize visual relations in
comparison to the ResNet-50 baseline. However, object-centric models still
struggle in our more difficult tasks and conditions. We conclude that abstract
visual reasoning remains an open challenge for DNNs, including object-centric
models.