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Skeleton Based Action Recognition On Sysu 3D

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

Paper TitleRepository
SGN86.9%Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
VA-fusion (aug.)86.7%View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition
EleAtt-GRU (aug.)85.7%EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks-
Local+LGN83.14%Learning Latent Global Network for Skeleton-based Action Prediction-
Complete GR-GCN77.9%Optimized Skeleton-based Action Recognition via Sparsified Graph Regression-
VA-LSTM77.5%View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
DPRL76.9%Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition-
Dynamic Skeletons75.5%Jointly learning heterogeneous features for rgb-d activity recognition-
ST-LSTM (Tree)73.4%Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates-
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Skeleton Based Action Recognition On Sysu 3D | SOTA | HyperAI超神经