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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 291300 of 419 papers

TitleStatusHype
Self-Attention Network for Skeleton-based Human Action Recognition0
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition0
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning0
Context Aware Graph Convolution for Skeleton-Based Action Recognition0
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition0
Unifying Graph Embedding Features with Graph Convolutional Networks for Skeleton-based Action Recognition0
Action Recognition with Domain Invariant Features of Skeleton Image0
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