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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 101110 of 419 papers

TitleStatusHype
SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational AutoencodersCode1
Simplifying Graph Convolutional NetworksCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Skeleton-based Action Recognition with Convolutional Neural NetworksCode1
Skeleton Aware Multi-modal Sign Language RecognitionCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
Spatial Temporal Graph Attention Network for Skeleton-Based Action RecognitionCode1
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning PotentialCode1
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