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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
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
Skeleton-Based Human Action Recognition with Noisy LabelsCode0
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner0
SkateFormer: Skeletal-Temporal Transformer for Human Action RecognitionCode2
Taylor Videos for Action RecognitionCode1
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition0
AutoGCN -- Towards Generic Human Activity Recognition with Neural Architecture SearchCode0
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition0
Unsupervised Spatial-Temporal Feature Enrichment and Fidelity Preservation Network for Skeleton based Action Recognition0
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