The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks
Open accessAbstract
Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, global context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a global context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
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Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183112 · Cited by 601
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
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition
Ye, F., Pu, S., Zhong, Q., Li, C., Xie, D., & Tang, H. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413941 · Cited by 341
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Skeleton Image Representation for 3D Action Recognition Based on Tree Structure and Reference Joints
Caetano, C., Bremond, F., & Schwartz, W. R. (2019) · 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2019.00011 · Cited by 149
Metadata
- Authors
- Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2018-04
- Volume
- 27
- Issue
- 4
- Pages
- 1586-1599
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2739179646
- Cited by
- 537
- References
- 66
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- green
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- Liu, J., Wang, G., Duan, L.-Y., Abdiyeva, K., & Kot, A. C. (2018). Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2017.2785279
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