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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 201210 of 419 papers

TitleStatusHype
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
The Multi-Modal Video Reasoning and Analyzing Competition0
Learning Skeletal Graph Neural Networks for Hard 3D Pose EstimationCode0
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
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
VPN++: Rethinking Video-Pose embeddings for understanding Activities of Daily LivingCode1
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