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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 141150 of 419 papers

TitleStatusHype
Hypergraph Transformer for Skeleton-based Action RecognitionCode1
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition0
MotionBERT: A Unified Perspective on Learning Human Motion RepresentationsCode3
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action RecognitionCode0
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition0
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition0
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning0
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition0
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action RecognitionCode0
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition0
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