Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …

Action recognition is a major branch of computer vision research. As a widely used
technology, action recognition has been applied to human–computer interaction, intelligent …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

In the semi-supervised skeleton-based action recognition task, obtaining more
discriminative information from both labeled and unlabeled data is a challenging problem …

The self-supervised pretraining paradigm has achieved great success in skeleton-based
action recognition. However, these methods treat the motion and static parts equally, and …

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

In recent years, self-supervised representation learning for skeleton-based action
recognition has been developed with the advance of contrastive learning methods. The …

Previous electroencephalogram (EEG) emotion recognition relies on single-task learning,
which may lead to overfitting and learned emotion features lacking generalization. In this …

In 3D human action recognition, limited supervised data makes it challenging to fully tap into
the modeling potential of powerful networks such as transformers. As a result, researchers …

Contrastive learning has been successfully leveraged to learn action representations for
addressing the problem of semisupervised skeleton-based action recognition. However …