Human action recognition from various data modalities: A review

Z Sun, Q Ke, H Rahmani, M Bennamoun… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Convolutional neural networks or vision transformers: Who will win the race for action recognitions in visual data?

O Moutik, H Sekkat, S Tigani, A Chehri, R Saadane… - Sensors, 2023 - mdpi.com
Understanding actions in videos remains a significant challenge in computer vision, which
has been the subject of several pieces of research in the last decades. Convolutional neural …

Revisiting skeleton-based action recognition

H Duan, Y Zhao, K Chen, D Lin… - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
Human skeleton, as a compact representation of human action, has received increasing
attention in recent years. Many skeleton-based action recognition methods adopt GCNs to …

Infogcn: Representation learning for human skeleton-based action recognition

H Chi, MH Ha, S Chi, SW Lee… - Proceedings of the …, 2022 - openaccess.thecvf.com
Human skeleton-based action recognition offers a valuable means to understand the
intricacies of human behavior because it can handle the complex relationships between …

Learning discriminative representations for skeleton based action recognition

H Zhou, Q Liu, Y Wang - … of the IEEE/CVF Conference on …, 2023 - openaccess.thecvf.com
Human action recognition aims at classifying the category of human action from a segment
of a video. Recently, people have dived into designing GCN-based models to extract …

Star-transformer: a spatio-temporal cross attention transformer for human action recognition

D Ahn, S Kim, H Hong, BC Ko - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In action recognition, although the combination of spatio-temporal videos and skeleton
features can improve the recognition performance, a separate model and balancing feature …

Multi-granularity anchor-contrastive representation learning for semi-supervised skeleton-based action recognition

X Shu, B Xu, L Zhang, J Tang - IEEE Transactions on Pattern …, 2022 - ieeexplore.ieee.org
In the semi-supervised skeleton-based action recognition task, obtaining more
discriminative information from both labeled and unlabeled data is a challenging problem …

Motionbert: A unified perspective on learning human motion representations

W Zhu, X Ma, Z Liu, L Liu, W Wu… - Proceedings of the …, 2023 - openaccess.thecvf.com
We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

Skeletonmae: graph-based masked autoencoder for skeleton sequence pre-training

H Yan, Y Liu, Y Wei, Z Li, G Li… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Skeleton sequence representation learning has shown great advantages for action
recognition due to its promising ability to model human joints and topology. However, the …

A union of deep learning and swarm-based optimization for 3D human action recognition

H Basak, R Kundu, PK Singh, MF Ijaz, M Woźniak… - Scientific Reports, 2022 - nature.com
Abstract Human Action Recognition (HAR) is a popular area of research in computer vision
due to its wide range of applications such as surveillance, health care, and gaming, etc …