  Capsule networks promise significant benefits over convolutional networks by
storing stronger internal representations, and routing information based on the
agreement between intermediate representations' projections. Despite this,
their success has been mostly limited to small-scale classification datasets
due to their computationally expensive nature. Recent studies have partially
overcome this burden by locally-constraining the dynamic routing of features
with convolutional capsules. Though memory efficient, convolutional capsules
impose geometric constraints which fundamentally limit the ability of capsules
to model the pose/deformation of objects. Further, they do not address the
bigger memory concern of class-capsules scaling-up to bigger tasks such as
detection or large-scale classification. In this study, we introduce deformable
capsules (DeformCaps), a new capsule structure (SplitCaps), and a novel dynamic
routing algorithm (SE-Routing) to balance computational efficiency with the
need for modeling a large number of objects and classes. We demonstrate that
the proposed methods allow capsules to efficiently scale-up to large-scale
computer vision tasks for the first time, and create the first-ever capsule
network for object detection in the literature. Our proposed architecture is a
one-stage detection framework and obtains results on MS COCO which are on-par
with state-of-the-art one-stage CNN-based methods, while producing fewer false
positive detections.
