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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 281290 of 419 papers

TitleStatusHype
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition0
Skeletonnet: Mining deep part features for 3-d action recognition0
SkeleTR: Towards Skeleton-based Action Recognition in the Wild0
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild0
Sparse Semi-Supervised Action Recognition with Active Learning0
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition0
Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition0
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition0
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition0
Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition0
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