Skeleton Based Action Recognition On Sbu
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
模型名称 | Accuracy | Paper Title | Repository |
|---|---|---|---|
| MLGCN | 98.60% | MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition | - |
| ArmaConv | 96.00% | Graph Neural Networks with convolutional ARMA filters | |
| VA-fusion (aug.) | 98.3% | View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition | |
| e2eET | 93.96 | Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN | |
| Joint Line Distance | 99.02% | On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks | |
| DeepGRU | 95.7% | DeepGRU: Deep Gesture Recognition Utility | |
| ChebyNet | 96.00% | Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | |
| ST-LSTM + Trust Gate | 93.3% | Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition | - |
| SGCConv | 94.0% | Simplifying Graph Convolutional Networks |
0 of 9 row(s) selected.