Capsule networks have recently gained a great deal of interest as a new architecture of neural networks that can be more robust to input perturbations than similar-sized CNNs. Capsule networks have two major distinctions from the conventional CNNs: (i) each layer consists of a set of capsules that specialize in disjoint regions of the feature space and (ii) the routing-by-agreement coordinates connections between adjacent capsule layers. Although the routing-by-agreement is capable of filtering out noisy predictions of capsules by dynamically adjusting their influences, its unsupervised clustering nature causes two weaknesses: (i) high computational complexity and (ii) cluster assumption that may not hold in presence of heavy input noise. In this work, we propose a novel and surprisingly simple routing strategy called self-routing where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR-10, SVHN and SmallNORB show that the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.

Capsule networks have been shown to be powerful models for image classification, thanks to their ability to represent and capture viewpoint variations of an object. However, the high computational complexity of capsule networks that stems from the recurrent dynamic routing poses a major drawback making their use for large-scale image classification challenging. In this work, we propose Star-Caps a capsule-based network that exploits a straight-through attentive routing to address the drawbacks of capsule networks. By utilizing attention modules augmented by differentiable binary routers, the proposed mechanism estimates the routing coefficients between capsules without recurrence, as opposed to prior related work. Subsequently, the routers utilize straight-through estimators to make binary decisions to either connect or disconnect the route between capsules, allowing stable and faster performance. The experiments conducted on several image classification datasets, including MNIST, SmallNorb, CIFAR-10, CIFAR-100, and ImageNet show that Star-Caps outperforms the baseline capsule networks.

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input.

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input.

We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.

Neural Information Processing Systems http://nips.cc/

Capsule networks have recently gained a great deal of interest as a new architecture of neural networks that can be more robust to input perturbations than similar-sized CNNs.

By throwing away information about the precise position of an entity, max-pooling achieves some translation invariance.

Experiments on MNIST/CIFAR10 showed very poor performance.

We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.