A novel approach which jointly optimizes a canonical time and pose configuration coupled with temporal and cyclic consistency constraints providing high quality outputs as the observed views become sparser.

Emergent communication via mid-level patches in a referential game played on a large-scale image dataset.

CapsNet based a finer-grained, motion-specific, better-generalizable optical flow estimation with lesser ground truth data and computational complexity.

A novel convolutional capsule architecture with a custom routing algorithm for raw point cloud processing.

A novel end-to-end deep learning framework that utilizes the changes in orthogonal frequency division multiplexing (OFDM) sub-carrier amplitude information to simultaneously predict the identity, activity and the trajectory of a user and create a user profile that is of similar utility to a one made through a video camera based approach.

Capsule Networks are cool, but they are shallow. We can increase the depth with the aid of 3D convolutions and skip connections.

Capsule Networks can capture the subtle variations present in human handwriting with a very few samples, which can be used to generate new realistic data.

Proposing tree-based capsule networks for processing program code in an automated way that encodes code syntactical structures and captures code dependencies more accurately.

Giving CapsNets the ability to understand 1D temporal relationships. We generate capsules along the temporal and channel dimensions creating two temporal feature detectors which learn contrasting relationships.

A web service tool, named SMARTEMBED, which can help Solidity developers to find repetitive contract code and clone-related bugs in smart contracts.

Three schemas for combining static and motion features in videos: based on a variance ratio, principal components, and Cholesky decomposition.

Extracting motion features at micro level, preserving the actor identity, to later obtain a high-level motion descriptor using a probabilistic model.