Papers › Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang
3D action recognition - analysis of human actions based on 3D skeleton data - becomes popular recently due to its succinctness, robustness, and view-invariant representation. Recent attempts on this problem suggested to develop RNN-based learning methods to model the contextual dependency in the temporal domain. In this paper, we extend this idea to spatio-temporal domains to analyze the hidden sources of action-related information within the input data over both domains concurrently. Inspired by the graphical structure of the human skeleton, we further propose a more powerful tree-structure based traversal method. To handle the noise and occlusion in 3D skeleton data, we introduce new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell. Our method achieves state-of-the-art performance on 4 challenging benchmark datasets for 3D human action analysis.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Skeleton Based Action Recognition | NTU RGB+D | Spatio-Temporal LSTM | Accuracy (CS) | 69.2 | #127 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | Spatio-Temporal LSTM | Accuracy (CV) | 77.7 | #127 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | ST-LSTM | Accuracy (CS) | 61.70 | #129 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | ST-LSTM | Accuracy (CV) | 75.50 | #129 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | Spatio-Temporal LSTM | Accuracy (Cross-Setup) | 57.9% | #80 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | Spatio-Temporal LSTM | Accuracy (Cross-Subject) | 55.7% | #80 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | SBU / SBU-Refine | ST-LSTM + Trust Gate | Accuracy | 93.3% | #9 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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