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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 5160 of 419 papers

TitleStatusHype
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
Learning Discriminative Representations for Skeleton Based Action RecognitionCode1
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action RecognitionCode1
Make Skeleton-based Action Recognition Model Smaller, Faster and BetterCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D VideosCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
Graph Attention NetworksCode1
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