Device-free activity recognition plays a crucial role in smart building, security, and human–
computer interaction, which shows its strength in its convenience and cost-efficiency …

Humans view the world through many sensory channels, eg, the long-wavelength light
channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right …

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

Large-scale labeled data are generally required to train deep neural networks in order to
obtain better performance in visual feature learning from images or videos for computer …

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D
skeleton-based action representation (CrosSCLR), by leveraging multi-view complementary …

We propose a self-supervised spatiotemporal learning technique which leverages the
chronological order of videos. Our method can learn the spatiotemporal representation of …

Human activity recognition has been actively studied in the last three decades. Compared to
human action performed by a single person, human interaction is more complex due to the …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

Leveraging physical knowledge described by partial differential equations (PDEs) is an
appealing way to improve unsupervised video forecasting models. Since physics is too …

Skeleton-based human action recognition has attracted increasing attention in recent years.
However, most of the existing works focus on supervised learning which requiring a large …