The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Open accessAbstract
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 dataset of SBU and the currently largest NTU dataset.
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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A survey on vision-based human action recognition
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Cited by
A review on the attention mechanism of deep learning
Niu, Z., Zhong, G., & Yu, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.03.091 · Cited by 3,379
Attention mechanisms in computer vision: A survey
Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022) · Computational Visual Media · doi:10.1007/s41095-022-0271-y · Cited by 2,566
Self-citationIndependently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Li, S., Li, W., Cook, C., Zhu, C., & Gao, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00572 · Cited by 972
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00132 · Cited by 917
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
Metadata
- Authors
- Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-12
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2950568498
- Cited by
- 977
- References
- 4
- FWCI
- 32.60
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2017). An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v31i1.11212
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