Papers › An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Sijie Song, Cuiling Lan, Junliang Xing, Wen-Jun Zeng, Jiaying Liu
Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model,both on the small human action recognition data set of SBU and the currently largest NTU dataset.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
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 | STA-LSTM | Accuracy (CS) | 73.4 | #124 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | STA-LSTM | Accuracy (CV) | 81.2 | #124 of 135 | 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
Introduced by this paper: STA-LSTM, Spatial & Temporal Attention
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections