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Skeleton Based Action Recognition
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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.
<span style="color:grey; opacity: 0.6">( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions
Unified Keypoint-based Action Recognition Framework via Structured Keypoint Pooling
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition
HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations
Learning Discriminative Representations for Skeleton Based Action Recognition
Spatial-temporal Transformer-guided Diffusion based Data Augmentation for Efficient Skeleton-based Action Recognition
Temporal-Channel Topology Enhanced Network for Skeleton-Based Action Recognition
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action Recognition
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)
Action Capsules: Human Skeleton Action Recognition
Skeleton-based Action Recognition through Contrasting Two-Stream Spatial-Temporal Networks
Graph Contrastive Learning for Skeleton-based Action Recognition
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations
Hypergraph Transformer for Skeleton-based Action Recognition
Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition
DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition
MotionBERT: A Unified Perspective on Learning Human Motion Representations
Pose-Guided Graph Convolutional Networks for Skeleton-Based Action Recognition
Focal and Global Spatial-Temporal Transformer for Skeleton-based Action Recognition
View-Invariant Skeleton-based Action Recognition via Global-Local Contrastive Learning
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-supervised Skeleton Action Recognition
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition
Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition
PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.