  Visual reasoning is a long-term goal of vision research. In the last decade,
several works have attempted to apply deep neural networks (DNNs) to the task
of learning visual relations from images, with modest results in terms of the
generalization of the relations learned. In recent years, several innovations
in DNNs have been developed in order to enable learning abstract relation from
images. In this work, we systematically evaluate a series of DNNs that
integrate mechanism such as slot attention, recurrently guided attention, and
external memory, in the simplest possible visual reasoning task: deciding
whether two objects are the same or different. We found that, although some
models performed better than others in generalizing the same-different relation
to specific types of images, no model was able to generalize this relation
across the board. We conclude that abstract visual reasoning remains largely an
unresolved challenge for DNNs.
