Papers › Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Jun Liu, Gang Wang, Ping Hu, Ling-Yu Duan, Alex C. Kot
Long Short-Term Memory (LSTM) networks have shown superior performance in 3D human action recognition due to their power in modeling the dynamics and dependencies in sequential data. Since not all joints are informative for action analysis and the irrelevant joints often bring a lot of noise, we need to pay more attention to the informative ones. However, original LSTM does not have strong attention capability. Hence we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for 3D action recognition, which is able to selectively focus on the informative joints in the action sequence with the assistance of global contextual information. In order to achieve a reliable attention representation for the action sequence, we further propose a recurrent attention mechanism for our GCA-LSTM network, in which the attention performance is improved iteratively. Experiments show that our end-to-end network can reliably focus on the most informative joints in each frame of the skeleton sequence. Moreover, our network yields state-of-the-art performance on three challenging datasets for 3D action recognition.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| One-Shot 3D Action Recognition | NTU RGB+D 120 | Fully Connected | Accuracy | 42.1% | #9 of 10 | Archive leaderboard | report |
| One-Shot 3D Action Recognition | NTU RGB+D 120 | Attention Network | Accuracy | 41.0% | #10 of 10 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | GCA-LSTM | Accuracy (CS) | 76.10 | #119 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | GCA-LSTM | Accuracy (CV) | 84.00 | #119 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | GCA-LSTM | Accuracy (Cross-Setup) | 59.2% | #78 of 83 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D 120 | GCA-LSTM | Accuracy (Cross-Subject) | 58.3% | #78 of 83 | 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.
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