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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 111120 of 419 papers

TitleStatusHype
VSViG: Real-time Video-based Seizure Detection via Skeleton-based Spatiotemporal ViGCode1
MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D VideosCode1
Skeleton-Contrastive 3D Action Representation LearningCode1
Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning PotentialCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Zero-shot Skeleton-based Action Recognition with Prototype-guided Feature AlignmentCode1
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