  Although convolutional neural networks (CNNs) showed remarkable results in
many vision tasks, they are still strained by simple yet challenging visual
reasoning problems. Inspired by the recent success of the Transformer network
in computer vision, in this paper, we introduce the Recurrent Vision
Transformer (RViT) model. Thanks to the impact of recurrent connections and
spatial attention in reasoning tasks, this network achieves competitive results
on the same-different visual reasoning problems from the SVRT dataset. The
weight-sharing both in spatial and depth dimensions regularizes the model,
allowing it to learn using far fewer free parameters, using only 28k training
samples. A comprehensive ablation study confirms the importance of a hybrid CNN
+ Transformer architecture and the role of the feedback connections, which
iteratively refine the internal representation until a stable prediction is
obtained. In the end, this study can lay the basis for a deeper understanding
of the role of attention and recurrent connections for solving visual abstract
reasoning tasks.
