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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 171180 of 419 papers

TitleStatusHype
STEP CATFormer: Spatial-Temporal Effective Body-Part Cross Attention Transformer for Skeleton-based Action RecognitionCode0
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System0
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-SupervisionCode0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition0
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
Local Spherical Harmonics Improve Skeleton-Based Hand Action RecognitionCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning0
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