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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 6170 of 419 papers

TitleStatusHype
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action RecognitionCode1
Skeleton-based Action Recognition via Temporal-Channel AggregationCode1
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D VideosCode1
A Spatio-Temporal Multilayer Perceptron for Gesture RecognitionCode1
Continual Spatio-Temporal Graph Convolutional NetworksCode1
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse OcclusionsCode1
Spatio-Temporal Tuples Transformer for Skeleton-Based Action RecognitionCode1
InfoGCN: Representation Learning for Human Skeleton-Based Action RecognitionCode1
Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action RecognitionCode1
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