From Patches to Objects: Exploiting Spatial Reasoning for Better Visual
  Representations
Toni Albert Bjoern Eskofier Dario Zanca
As the field of deep learning steadily transitions from the realm of academic
research to practical application, the significance of self-supervised
pretraining methods has become increasingly prominent. These methods,
particularly in the image domain, offer a compelling strategy to effectively
utilize the abundance of unlabeled image data, thereby enhancing downstream
tasks' performance. In this paper, we propose a novel auxiliary pretraining
method that is based on spatial reasoning. Our proposed method takes advantage
of a more flexible formulation of contrastive learning by introducing spatial
reasoning as an auxiliary task for discriminative self-supervised methods.
Spatial Reasoning works by having the network predict the relative distances
between sampled non-overlapping patches. We argue that this forces the network
to learn more detailed and intricate internal representations of the objects
and the relationships between their constituting parts. Our experiments
demonstrate substantial improvement in downstream performance in linear
evaluation compared to similar work and provide directions for further research
into spatial reasoning.