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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 8190 of 419 papers

TitleStatusHype
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Masked Motion Predictors are Strong 3D Action Representation LearnersCode1
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action RecognitionCode1
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action RecognitionCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
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