The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Abstract
Skeleton-based action recognition is an important task that requires the adequate understanding of movement characteristics of a human action from the given skeleton sequence. Recent studies have shown that exploring spatial and temporal features of the skeleton sequence is vital for this task. Nevertheless, how to effectively extract discriminative spatial and temporal features is still a challenging problem. In this paper, we propose a novel Attention Enhanced Graph Convolutional LSTM Network (AGC-LSTM) for human action recognition from skeleton data. The proposed AGC-LSTM can not only capture discriminative features in spatial configuration and temporal dynamics but also explore the co-occurrence relationship between spatial and temporal domains. We also present a temporal hierarchical architecture to increase temporal receptive fields of the top AGC-LSTM layer, which boosts the ability to learn the high-level semantic representation and significantly reduces the computation cost. Furthermore, to select discriminative spatial information, the attention mechanism is employed to enhance information of key joints in each AGC-LSTM layer. Experimental results on two datasets are provided: NTU RGB+D dataset and Northwestern-UCLA dataset. The comparison results demonstrate the effectiveness of our approach and show that our approach outperforms the state-of-the-art methods on both datasets.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · cited 1×
“Besides, the Microsoft Kinect [37] and advanced human pose estimation algorithms [2] make it easier to gain skeleton data.”
BackgroundTwo-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Yan, S., Xiong, Y., & Lin, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12328 · Cited by 5,410
Visual perception of biological motion and a model for its analysis
Johansson, G. (1973) · Perception & Psychophysics · doi:10.3758/bf03212378 · Cited by 4,462
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., & Van Gool, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_2 · Cited by 3,958
Self-citationFormal Semantics for Kolmogorov-Arnold Network Representations of Operational Games
Zhukov, G. A. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.15206559 · Cited by 3,212
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Shahroudy, A., Liu, J., Ng, T.-T., & Wang, G. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.115 · Cited by 3,148
Microsoft Kinect Sensor and Its Effect
Zhang, Z. (2012) · IEEE Multimedia · doi:10.1109/mmul.2012.24 · Cited by 2,487
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- duvenaud, 2015, Convolutional networks on graphs for learning molecular fingerprints, NIPS
- thakkar, 2018, Part-based graph convolutional network for action recognition, BMVC
Cited by
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
Song, Y.-F., Zhang, Z., Shan, C., & Wang, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3157033 · Cited by 528
Self-citationSkeleton-based action recognition via spatial and temporal transformer networks
Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Cheng, K., Zhang, Y., Cao, C., Shi, L., Cheng, J., & Lu, H. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58586-0_32 · Cited by 384
Spatiotemporal Co-Attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
Shu, X., Zhang, L., Qi, G.-J., Liu, W., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050918 · Cited by 276
3D Human Action Representation Learning via Cross-View Consistency Pursuit
Li, L., Wang, M., Ni, B., Wang, H., Yang, J., & Zhang, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00471 · Cited by 236
“Many 3D action recognition works [6, 60, 17, 25, 44, 28, 16] use a fully-supervised manner and require massive labeled 3D skeleton data.”
BackgroundMethod
Metadata
- Authors
- Si, C., Chen, W., Wang, W., Wang, L., & Tan, T.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1227-1236
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963465695
- Cited by
- 917
- References
- 42
- FWCI
- 40.73
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Si, C., Chen, W., Wang, W., Wang, L., & Tan, T. (2019). An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00132
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