Human action recognition from various data modalities: A review
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 …
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?
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 …
has been the subject of several pieces of research in the last decades. Convolutional neural …
Star-transformer: a spatio-temporal cross attention transformer for human action recognition
In action recognition, although the combination of spatio-temporal videos and skeleton
features can improve the recognition performance, a separate model and balancing feature …
features can improve the recognition performance, a separate model and balancing feature …
Hierarchically decomposed graph convolutional networks for skeleton-based action recognition
Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-
based action recognition and have achieved remarkable performance. Generating …
based action recognition and have achieved remarkable performance. Generating …
Masked motion predictors are strong 3d action representation learners
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 …
the modeling potential of powerful networks such as transformers. As a result, researchers …
Generative action description prompts for skeleton-based action recognition
Skeleton-based action recognition has recently received considerable attention. Current
approaches to skeleton-based action recognition are typically formulated as one-hot …
approaches to skeleton-based action recognition are typically formulated as one-hot …
3mformer: Multi-order multi-mode transformer for skeletal action recognition
Many skeletal action recognition models use GCNs to represent the human body by 3D
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …
body joints connected body parts. GCNs aggregate one-or few-hop graph neighbourhoods …
Unified pose sequence modeling
Abstract We propose a Unified Pose Sequence Modeling approach to unify heterogeneous
human behavior understanding tasks based on pose data, eg, action recognition, 3D pose …
human behavior understanding tasks based on pose data, eg, action recognition, 3D pose …
Pyramid self-attention polymerization learning for semi-supervised skeleton-based action recognition
Most semi-supervised skeleton-based action recognition approaches aim to learn the
skeleton action representations only at the joint level, but neglect the crucial motion …
skeleton action representations only at the joint level, but neglect the crucial motion …
Skateformer: skeletal-temporal transformer for human action recognition
Skeleton-based action recognition, which classifies human actions based on the
coordinates of joints and their connectivity within skeleton data, is widely utilized in various …
coordinates of joints and their connectivity within skeleton data, is widely utilized in various …