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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 411419 of 419 papers

TitleStatusHype
DeepGRU: Deep Gesture Recognition UtilityCode0
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton SequenceCode0
Action Recognition with Multi-stream Motion Modeling and Mutual Information MaximizationCode0
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action RecognitionCode0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Cross-modal Learning by Hallucinating Missing Modalities in RGB-D VisionCode0
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints InvarianceCode0
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action RecognitionCode0
Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringCode0
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