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

Showing 7180 of 419 papers

TitleStatusHype
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Sign Language Recognition via Skeleton-Aware Multi-Model EnsembleCode1
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional NetworksCode1
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action RecognitionCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
UNIK: A Unified Framework for Real-world Skeleton-based Action RecognitionCode1
Show:102550
← PrevPage 8 of 42Next →

No leaderboard results yet.