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 111120 of 419 papers

TitleStatusHype
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action RepresentationsCode1
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action RecognitionCode1
Semi-Supervised Classification with Graph Convolutional NetworksCode1
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Hypergraph Transformer for Skeleton-based Action RecognitionCode1
Traffic Control Gesture Recognition for Autonomous VehiclesCode1
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning PotentialCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature AlignmentCode1
Show:102550
← PrevPage 12 of 42Next →

No leaderboard results yet.