In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

Although widely used in many applications, accurate and efficient human action recognition
remains a challenging area of research in the field of computer vision. Most recent surveys …

Video coding, which targets to compress and reconstruct the whole frame, and feature
compression, which only preserves and transmits the most critical information, stand at two …

Human action analytics has attracted a lot of attention for decades in computer vision. It is
important to extract discriminative spatio-temporal features to model the spatial and temporal …

For skeleton-based action recognition, most of the existing works used recurrent neural
networks. Using convolutional neural networks (CNNs) is another attractive solution …

Video saliency prediction (VSP) aims to imitate eye fixations of humans. However, the
potential of this task has not been fully exploited since existing VSP methods only focus on …

Human activity recognition is one of the most studied topics in the field of computer vision. In
recent years, with the availability of RGB-D sensors and powerful deep learning techniques …

To meet the demand for powerful models for practical applications in real time, the focus of
research on human actions has shifted from offline detection to online and real-time …

Single-modality human action recognition on RGB or skeleton has been extensively studied.
Each of these two modalities has its own advantages as well as limitations, because they …

Online action detection aims at detecting the ongoing action in a streaming video. In this
paper, we proposed an uncertainty-based spatial-temporal attention for online action …