Skeleton-based multi-stream adaptive-attentional sub-graph convolution network for action recognition
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-15778-z.pdf
Reference59 articles.
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2. Cai X, Zhou W, Wu L, Luo J, Li H (2016) Effective active skeleton representation for low latency human action recognition. IEEE Trans Multimed 141-154. https://ieeexplore.ieee.org/document/7346460
3. Carreira J, Zisserman A (2017) Quo Vadis, action recognition? A new model and the kinetics dataset. 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 6299–6308. https://ieeexplore.ieee.org/document/8099985
4. Chao L, Zhong Q, Di X, Pu S (2017) Skeleton-based action recognition with convolutional neural networks. 2017 IEEE international conference on multimedia & expo workshops (ICMEW), pp 597-600. https://ieeexplore.ieee.org/document/8026285
5. Chen C (2015) UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. 2015 IEEE international conference on image processing (ICIP), pp 168-172. https://ieeexplore.ieee.org/document/7350781
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1. RE-STNet: relational enhancement spatio-temporal networks based on skeleton action recognition;Multimedia Tools and Applications;2024-03-15
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