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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 8190 of 419 papers

TitleStatusHype
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System0
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action RecognitionCode1
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes0
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-SupervisionCode0
Exploring Self-supervised Skeleton-based Action Recognition in Occluded EnvironmentsCode1
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild0
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action RecognitionCode1
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition0
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action RecognitionCode1
SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action RecognitionCode1
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