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 211220 of 419 papers

TitleStatusHype
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition0
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action RecognitionCode0
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition0
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition0
AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement0
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action RecognitionCode0
Skeletal Human Action Recognition using Hybrid Attention based Graph Convolutional NetworkCode0
Show:102550
← PrevPage 22 of 42Next →

No leaderboard results yet.