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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 201210 of 419 papers

TitleStatusHype
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action RecognitionCode0
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition0
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)0
Action Capsules: Human Skeleton Action Recognition0
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild0
Parallel Attention Interaction Network for Few-Shot Skeleton-Based Action Recognition0
Modeling the Relative Visual Tempo for Self-supervised Skeleton-based Action RecognitionCode0
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition0
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action RecognitionCode0
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