Deep reinforcement learning augments the reinforcement learning framework and utilizes
the powerful representation of deep neural networks. Recent works have demonstrated the …

Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Sign language is commonly used by deaf or speech impaired people to communicate but
requires significant effort to master. Sign Language Recognition (SLR) aims to bridge the …

With the growing use of graph convolutional neural networks (GCNNs) comes the need for
explainability. In this paper, we introduce explainability methods for GCNNs. We develop the …

Dynamics of human body skeletons convey significant information for human action
recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

The translation equivariance of convolutional layers enables CNNs to generalize well on
image problems. While translation equivariance provides a powerful inductive bias for …

In this paper, we propose a deep progressive reinforcement learning (DPRL) method for
action recognition in skeleton-based videos, which aims to distil the most informative frames …

Skeleton-based human action recognition has attracted a lot of research attention during the
past few years. Recent works attempted to utilize recurrent neural networks to model the …