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 101110 of 419 papers

TitleStatusHype
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action RecognitionCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
Large-Scale Video Classification with Convolutional Neural NetworksCode1
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action RecognitionCode1
Spatial Temporal Transformer Network for Skeleton-based Action RecognitionCode1
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action RecognitionCode1
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning PotentialCode1
Show:102550
← PrevPage 11 of 42Next →

No leaderboard results yet.