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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 4150 of 419 papers

TitleStatusHype
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action RecognitionCode1
A Spatio-Temporal Multilayer Perceptron for Gesture RecognitionCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
InfoGCN: Representation Learning for Human Skeleton-Based Action RecognitionCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Graph Attention NetworksCode1
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