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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 231240 of 419 papers

TitleStatusHype
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition0
Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action RecognitionCode0
SAR-NAS: Skeleton-based Action Recognition via Neural Architecture Searching0
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition0
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action RecognitionCode1
Pose And Joint-Aware Action RecognitionCode0
Pose Refinement Graph Convolutional Network for Skeleton-based Action Recognition0
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Effective Action Recognition with Embedded Key Point Shifts0
Skeleton-based Action Recognition via Spatial and Temporal Transformer NetworksCode1
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