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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 321330 of 419 papers

TitleStatusHype
Unsupervised Motion Representation Learning with Capsule AutoencodersCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Skeleton-Based Action Recognition with Spatial-Structural Graph ConvolutionCode0
Skeleton-Based Action Recognition With Directed Graph Neural NetworksCode0
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional NetworksCode0
Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence CriterionCode0
Graph Neural Networks with convolutional ARMA filtersCode0
View-Invariant Probabilistic Embedding for Human PoseCode0
A Central Difference Graph Convolutional Operator for Skeleton-Based Action RecognitionCode0
Skeleton-Based Human Action Recognition with Noisy LabelsCode0
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