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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 51–100 of 419 papers

TitleStatusHype
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language RecognitionCode1
Sign Language Recognition via Skeleton-Aware Multi-Model EnsembleCode1
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
Ske2Grid: Skeleton-to-Grid Representation Learning for Action RecognitionCode1
Graph Attention NetworksCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
Spatial Temporal Graph Attention Network for Skeleton-Based Action RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional NetworksCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing AugmentationsCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
InfoGCN: Representation Learning for Human Skeleton-Based Action RecognitionCode1
Infrared and 3D skeleton feature fusion for RGB-D action recognitionCode1
Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action SegmentationCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
Large-Scale Video Classification with Convolutional Neural NetworksCode1
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Make Skeleton-based Action Recognition Model Smaller, Faster and BetterCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action RecognitionCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Continual Spatio-Temporal Graph Convolutional NetworksCode1
Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action RecognitionCode1
Part Aware Contrastive Learning for Self-Supervised Action RecognitionCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
PREDICT & CLUSTER: Unsupervised Skeleton Based Action RecognitionCode1
A Spatio-Temporal Multilayer Perceptron for Gesture RecognitionCode1
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse OcclusionsCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNNCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
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