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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 91100 of 419 papers

TitleStatusHype
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
Local Spherical Harmonics Improve Skeleton-Based Hand Action RecognitionCode0
Ske2Grid: Skeleton-to-Grid Representation Learning for Action RecognitionCode1
Masked Motion Predictors are Strong 3D Action Representation LearnersCode1
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and MaximizationCode1
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-trainingCode1
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action RecognitionCode1
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning0
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition0
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