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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 401410 of 419 papers

TitleStatusHype
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition0
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions0
Adding Attentiveness to the Neurons in Recurrent Neural Networks0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition0
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning0
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition0
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