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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
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Anonymization for Skeleton Action RecognitionCode1
Part Aware Contrastive Learning for Self-Supervised Action RecognitionCode1
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
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
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