  The capsule network is a distinct and promising segment of the neural network
family that drew attention due to its unique ability to maintain the
equivariance property by preserving the spatial relationship amongst the
features. The capsule network has attained unprecedented success over image
classification tasks with datasets such as MNIST and affNIST by encoding the
characteristic features into the capsules and building the parse-tree
structure. However, on the datasets involving complex foreground and background
regions such as CIFAR-10, the performance of the capsule network is sub-optimal
due to its naive data routing policy and incompetence towards extracting
complex features. This paper proposes a new design strategy for capsule network
architecture for efficiently dealing with complex images. The proposed method
incorporates wide bottleneck residual modules and the Squeeze and Excitation
attention blocks upheld by the modified FM routing algorithm to address the
defined problem. A wide bottleneck residual module facilitates extracting
complex features followed by the squeeze and excitation attention block to
enable channel-wise attention by suppressing the trivial features. This setup
allows channel inter-dependencies at almost no computational cost, thereby
enhancing the representation ability of capsules on complex images. We
extensively evaluate the performance of the proposed model on three publicly
available datasets, namely CIFAR-10, Fashion MNIST, and SVHN, to outperform the
top-5 performance on CIFAR-10 and Fashion MNIST with highly competitive
performance on the SVHN dataset.
