SOTAVerified

Skeleton Based Action Recognition

Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 241–250 of 419 papers

TitleStatusHype
Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition—0
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action RecognitionCode0
IIP-Transformer: Intra-Inter-Part Transformer for Skeleton-Based Action Recognition—0
Unsupervised Motion Representation Learning with Capsule AutoencodersCode0
Adversarial Bone Length Attack on Action Recognition—0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition—0
The Multi-Modal Video Reasoning and Analyzing Competition—0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
Show:102550
← PrevPage 25 of 42Next →

No leaderboard results yet.