Action recognition via 3D skeleton data is an emerging important topic. Most existing
methods rely on hand-crafted descriptors to recognize actions, or perform supervised action …

Convolutional neural networks (CNNs) have come to dominate vision-based deep neural
network structures in both image and video models over the past decade. However …

Smart home and Artificial Intelligence (AI) expertise are quickly evolving, and a variety of
smart home appliances incorporating AI have improved occupant quality of life. Although …

Gesture recognition is a much studied research area which has myriad real-world
applications including robotics and human-machine interaction. Current gesture recognition …

Domain-invariant (view-invariant and modality-invariant) feature representation is essential
for human action recognition. Moreover, given a discriminative visual representation, it is …

Video datasets suffer from huge inter-frame redundancy, which prevents deep networks from
learning effectively and increases computational costs. Therefore, several methods adopt …

There are two competing standards for self-supervised learning in action recognition from
3D skeletons. Su et al., 2020 used an auto-encoder architecture and an image …

The ability to recognize actions throughout a video is essential for surveillance, self-driving,
and many other applications. Although many researchers have investigated deep neural …

Video-based action recognition is a challenging task, which demands carefully considering
the temporal property of videos in addition to the appearance attributes. Particularly, the …

Zero-shot learning (ZSL) typically explores a shared semantic space in order to recognize
novel categories in the absence of any labeled training data. However, the traditional ZSL …