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Skeleton Based Action Recognition On Sysu 3D

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
SGN	86.9%	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+LGN	83.14%	Learning Latent Global Network for Skeleton-based Action Prediction	-
Complete GR-GCN	77.9%	Optimized Skeleton-based Action Recognition via Sparsified Graph Regression	-
VA-LSTM	77.5%	View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data
DPRL	76.9%	Deep Progressive Reinforcement Learning for Skeleton-Based Action Recognition	-
Dynamic Skeletons	75.5%	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