  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 limited to small-scale classification datasets due to
their computationally expensive nature. Though memory efficient, convolutional
capsules impose geometric constraints that 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 a new
family of capsule networks, deformable capsules (\textit{DeformCaps}), to
address a very important problem in computer vision: object detection. We
propose two new algorithms associated with our \textit{DeformCaps}: a novel
capsule structure (\textit{SplitCaps}), and a novel dynamic routing algorithm
(\textit{SE-Routing}), which balance computational efficiency with the need for
modeling a large number of objects and classes, which have never been achieved
with capsule networks before. We demonstrate that the proposed methods
efficiently scale up to create the first-ever capsule network for object
detection in the literature. Our proposed architecture is a one-stage detection
framework and it obtains results on MS COCO which are on par with
state-of-the-art one-stage CNN-based methods, while producing fewer false
positive detection, generalizing to unusual poses/viewpoints of objects.
