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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.
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition
3D Skeleton-Based Action Recognition: A Review
Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition
SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation
Action Recognition in Real-World Ambient Assisted Living Environment
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action Recognition
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling
BlanketGen2-Fit3D: Synthetic Blanket Augmentation Towards Improving Real-World In-Bed Blanket Occluded Human Pose Estimation
HFGCN:Hypergraph Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition
IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
Improving Skeleton-based Action Recognition with Interactive Object Information
Evolving Skeletons: Motion Dynamics in Action Recognition
Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion
Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity Recognition
Synchronized and Fine-Grained Head for Skeleton-Based Ambiguous Action Recognition
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition
USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition
Recovering Complete Actions for Cross-dataset Skeleton Action Recognition
Idempotent Unsupervised Representation Learning for Skeleton-Based Action Recognition
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition
Spatial Hierarchy and Temporal Attention Guided Cross Masking for Self-supervised Skeleton-based Action Recognition
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment
SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action Recognition
Joint Temporal Pooling for Improving Skeleton-based Action Recognition
Action Recognition for Privacy-Preserving Ambient Assisted Living
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics
Skeleton-Based Action Recognition with Spatial-Structural Graph Convolution
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
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