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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.
SkelVIT: Consensus of Vision Transformers for a Lightweight Skeleton-Based Action Recognition System
Distribution of Action Movements (DAM): A Descriptor for Human Action Recognition
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action Recognition
Proving the Potential of Skeleton Based Action Recognition to Automate the Analysis of Manual Processes
Elevating Skeleton-Based Action Recognition with Efficient Multi-Modality Self-Supervision
Exploring Self-supervised Skeleton-based Action Recognition in Occluded Environments
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
Multi-Semantic Fusion Model for Generalized Zero-Shot Skeleton-Based Action Recognition
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition
SiT-MLP: A Simple MLP with Point-wise Topology Feature Learning for Skeleton-based Action Recognition
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action Recognition
Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition
Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
Masked Motion Predictors are Strong 3D Action Representation Learners
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training
Cross-Model Cross-Stream Learning for Self-Supervised Human Action Recognition
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action Recognition
Miniaturized Graph Convolutional Networks with Topologically Consistent Pruning
Multi-Dimensional Refinement Graph Convolutional Network with Robust Decouple Loss for Fine-Grained Skeleton-Based Action Recognition
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation
Action Recognition with Multi-stream Motion Modeling and Mutual Information Maximization
How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action Recognition
Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness
Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition
Part Aware Contrastive Learning for Self-Supervised Action Recognition
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization
Skeleton-based action analysis for ADHD diagnosis
Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.