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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 131140 of 419 papers

TitleStatusHype
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition0
Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence CriterionCode0
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity RecognitionCode0
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition0
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action RecognitionCode0
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action RecognitionCode0
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition0
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
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