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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

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Showing 201–250 of 419 papers

TitleStatusHype
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition—0
The Multi-Modal Video Reasoning and Analyzing Competition—0
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
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Revisiting Skeleton-based Action RecognitionCode1
Hierarchical growing grid networks for skeleton based action recognitionCode0
Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition—0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition—0
Skeleton Aware Multi-modal Sign Language RecognitionCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
Domain and View-point Agnostic Hand Action RecognitionCode0
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition—0
NTU-X: An Enhanced Large-scale Dataset for Improving Pose-based Recognition of Subtle Human ActionsCode1
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Action Recognition with Kernel-based Graph Convolutional Networks—0
Tensor Representations for Action RecognitionCode1
Temporal Graph Modeling for Skeleton-based Action Recognition—0
Spatial Temporal Transformer Network for Skeleton-based Action RecognitionCode1
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching—0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition—0
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
Pose And Joint-Aware Action RecognitionCode0
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition—0
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Effective Action Recognition with Embedded Key Point Shifts—0
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Richly Activated Graph Convolutional Network for Robust Skeleton-based Action RecognitionCode0
Graph Convolution with Low-rank Learnable Local FiltersCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
HARD-Net: Hardness-AwaRe Discrimination Network for 3D Early Activity Prediction—0
On Dropping Clusters to Regularize Graph Convolutional Neural Networks—0
Improving Skeleton-based Action Recognitionwith Robust Spatial and Temporal Features—0
Traffic Control Gesture Recognition for Autonomous VehiclesCode1
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition—0
Hierarchical Action Classification with Network Pruning—0
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
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