In recent years, graph convolutional networks (GCNs) play an increasingly critical role in
skeleton-based human action recognition. However, most GCN-based methods still have …

Group activities usually involve spatio-temporal dynamics among many interactive
individuals, while only a few participants at several key frames essentially define the activity …

Recent works have achieved remarkable performance for action recognition with human
skeletal data by utilizing graph convolutional models. Existing models mainly focus on …

Multimodal image fusion plays an essential role in medical image analysis and application,
where computed tomography (CT), magnetic resonance (MR), single-photon emission …

Skeleton-based human action recognition has attracted considerable attention and
achieved great success in several engineering fields, which is also one of the most active …

We focus on unsupervised representation learning for skeleton based action recognition.
Existing unsupervised approaches usually learn action representations by motion prediction …

In this paper, we propose a simple yet effective self-supervised method called spatio-
temporal contrastive learning (ST-CL) for 3D skeleton-based action recognition. ST-CL …

Skeleton data carries valuable motion information and is widely explored in human action
recognition. However, not only the motion information but also the interaction with the …

The hand gesture recognition system is a noncontact and intuitive communication approach,
which, in turn, allows for natural and efficient interaction. This work focuses on develo** a …

Targeted attack aims to mislead the classification model to a specific class, and it can be
further divided into black-box and white-box targeted attack depending on whether the …