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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 2130 of 419 papers

TitleStatusHype
USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature DecorrelationCode1
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action RecognitionCode2
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action RecognitionCode0
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action RecognitionCode1
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition0
Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action SegmentationCode1
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition0
Idempotent Unsupervised Representation Learning for Skeleton-Based Action RecognitionCode0
Joint Mixing Data Augmentation for Skeleton-based Action RecognitionCode0
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
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