The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
Abstract
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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Structural-RNN: Deep Learning on Spatio-Temporal Graphs
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End-to-End Learning of Action Detection from Frame Glimpses in Videos
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Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014
Human Action Recognition From Various Data Modalities: A Review
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-citation2D/3D Pose Estimation and Action Recognition Using Multitask Deep Learning
Luvizon, D. C., Picard, D., & Tabia, H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00539 · Cited by 555
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition
Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., & Zheng, N. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2896631 · Cited by 523
Skeleton-based action recognition via spatial and temporal transformer networks
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Metadata
- Authors
- Liu, J., Wang, G., Hu, P., Duan, L.-Y., & Kot, A. C.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 3671-3680
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2736334449
- Cited by
- 682
- References
- 7
- FWCI
- 20.79
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, J., Wang, G., Hu, P., Duan, L.-Y., & Kot, A. C. (2017). Global Context-Aware Attention LSTM Networks for 3D Action Recognition. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.391
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