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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 251260 of 419 papers

TitleStatusHype
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
VPN: Learning Video-Pose Embedding for Activities of Daily LivingCode1
Quo Vadis, Skeleton Action Recognition ?Code1
Subspace Clustering for Action Recognition with Covariance Representations and Temporal PruningCode1
Iterate & Cluster: Iterative Semi-Supervised Action RecognitionCode1
Context Aware Graph Convolution for Skeleton-Based Action Recognition0
Skeleton-Based Action Recognition With Shift Graph Convolutional NetworkCode1
Towards Understanding the Adversarial Vulnerability of Skeleton-based Action Recognition0
A Semantics-Guided Graph Convolutional Network for Skeleton-Based Action Recognition0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition0
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