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 …

Large-scale benchmarks provide a solid foundation for the development of action analytics.
Most of the previous activity benchmarks focus on analyzing actions in RGB videos. There is …

An unsupervised human action modeling framework can provide useful pose-sequence
representation, which can be utilized in a variety of pose analysis applications. In this work …

Recently, graph convolutional networks (GCNs) play a critical role in skeleton-based human
action recognition. However, most GCN-based methods still have two main limitations:(1) …

This paper presents a new framework for action recognition with multi-modal data. A
skeleton-indexed feature learning procedure is developed to further exploit the detailed …

In the world of action recognition research, one primary focus has been on how to construct
and train networks to model the spatial-temporal volume of an input video. These methods …

Recent studies have made great progress on skeleton-based action recognition. However,
most of them are developed with relatively clean skeletons without the presence of intensive …

Most action recognition solutions rely on dense sampling to precisely cover the informative
temporal clip. Extensively searching temporal region is expensive for a real-world …

This paper presents a new framework for human action recognition from a 3D skeleton
sequence. Previous studies do not fully utilize the temporal relationships between video …

This study aims at generating reliable augmented training data to learn a robust deep model
for action recognition. The prior knowledge inferred from few training data is not sufficient to …