ShaunYang1/Skeleton-based-Action-Recognition-Papers

"Whenever I feel the need to exercise, I lie down until it goes away."― Paul Terry

Skeleton based Action Recognition Papers

• Quo Vadis, Skeleton Action Recognition?
• A Comparative Review of Recent Kinect-based Action Recognition Algorithms
• Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
• NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
• A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition
• Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction
• Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
• Revisiting Skeleton-based Action Recognition
• Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition
• Part-based Graph Convolutional Network for Action Recognition
• Skeleton-Based Action Recognition with Shift Graph Convolutional Network
• NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
• Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints
• A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
• NTU60-X: Towards Skeleton-based Recognition of Subtle Human Actions
• Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction
• Decoupling GCN with Drop Graph Module for Skeleton-Based Action Recognition
• Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
• A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
• Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
• Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement
• Ensemble Deep Learning for Skeleton-based Action Recognition using Temporal Sliding LSTM networks
• View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
• View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
• Unsupervised Representation Learning with Long-Term Dynamics for Skeleton Based Action Recognition
• Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
• Leveraging Third-Order Features in Skeleton-Based Action Recognition
• An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
• PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
• Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
• Context Aware Graph Convolution for Skeleton-Based Action Recognition
• PREDICT & CLUSTER: Unsupervised Skeleton Based Action Recognition
• Graph CNNs with Motif and Variable Temporal Block for Skeleton-based Action Recognition
• Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition
• Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition
• SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition
• Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
• Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention
• Deep Independently Recurrent Neural Network (IndRNN)
• A New Representation of Skeleton Sequences for 3D Action Recognition
• Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition
• Optimized Skeleton-based Action Recognition via Sparsified Graph Regression
• Richly Activated Graph Convolutional Network for Action Recognition with Incomplete Skeletons
• Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition
• 3D Human Action Representation Learning via Cross-View Consistency Pursuit
• Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
• Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
• Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
• An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
• Jointly Learning Heterogeneous Features for RGB-D Activity Recognition
• Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
• MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
• Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks
• Syntactically Guided Generative Embeddings for Zero-Shot Skeleton Action Recognition
• Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
• Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
• Memory Attention Networks for Skeleton-based Action Recognition
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
• Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks
• Mix Dimension in Poincare Geometry for 3D Skeleton-based Action Recognition
• Skeleton-Based Action Recognition with Directed Graph Neural Networks
• Skeleton-based action recognition with convolutional neural networks
• Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
• BASAR: Black-box Attack on Skeletal Action Recognition
• Skeleton-based Action Recognition Using LSTM and CNN
• Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition
• Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
• Make Skeleton-based Action Recognition Model Smaller, Faster and Better