  Recently proposed Capsule Network is a brain inspired architecture that
brings a new paradigm to deep learning by modelling input domain variations
through vector based representations. Despite being a seminal contribution,
CapsNet does not explicitly model structured relationships between the detected
entities and among the capsule features for related inputs. Motivated by the
working of cortical network in human visual system, we seek to resolve CapsNet
limitations by proposing several intuitive modifications to the CapsNet
architecture. We introduce, (1) a novel routing weight initialization
technique, (2) an improved CapsNet design that exploits semantic relationships
between the primary capsule activations using a densely connected Conditional
Random Field and (3) a Cholesky transformation based correlation module to
learn a general priority scheme. Our proposed design allows CapsNet to scale
better to more complex problems, such as the multi-label classification task,
where semantically related categories co-exist with various interdependencies.
We present theoretical bases for our extensions and demonstrate significant
improvements on ADE20K scene dataset.
