| Paper | Code | Accuracy | ModelName | ReleaseDate |
|---|---|---|---|---|
| On Geometric Features for Skeleton-Based Action Recognition using Multilayer LSTM Networks | ✓ Link | 99.02% | Joint Line Distance | 2017-03-01 |
| MLGCN: Multi-Laplacian Graph Convolutional Networks for Human Action Recognition | 98.60% | MLGCN | 2019-09-11 | |
| View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition | ✓ Link | 98.3% | VA-fusion (aug.) | 2018-04-20 |
| Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | ✓ Link | 96.00% | ChebyNet | 2016-06-30 |
| Graph Neural Networks with convolutional ARMA filters | ✓ Link | 96.00% | ArmaConv | 2019-01-05 |
| DeepGRU: Deep Gesture Recognition Utility | ✓ Link | 95.7% | DeepGRU | 2018-10-30 |
| Simplifying Graph Convolutional Networks | ✓ Link | 94.0% | SGCConv | 2019-02-19 |
| Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream CNN | ✓ Link | 93.96 | e2eET | 2024-06-21 |
| Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition | 93.3% | ST-LSTM + Trust Gate | 2016-07-24 |