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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 51–75 of 419 papers

TitleStatusHype
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation LearningCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing AugmentationsCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action RepresentationsCode1
Quo Vadis, Action Recognition? A New Model and the Kinetics DatasetCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
InfoGCN: Representation Learning for Human Skeleton-Based Action RecognitionCode1
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action RecognitionCode1
Make Skeleton-based Action Recognition Model Smaller, Faster and BetterCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
Large-Scale Video Classification with Convolutional Neural NetworksCode1
Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action RecognitionCode1
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