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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
Part Aware Contrastive Learning for Self-Supervised Action RecognitionCode1
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning PotentialCode1
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization0
Skeleton-based action analysis for ADHD diagnosis0
Attack-Augmentation Mixing-Contrastive Skeletal Representation LearningCode0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling0
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition0
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