Temporal-visual proposal graph network for temporal action detection
Author:
Funder
the National Key R &D Program of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-04947-0.pdf
Reference59 articles.
1. Zhang X-Y, Huang Y-P, Mi Y, Pei Y-T, Zou Q, Wang S (2021) Video sketch: A middle-level representation for action recognition. Appl Intell 51(4):2589–2608. https://doi.org/10.1007/s10489-020-01905-y
2. Zhang C, Xu Y, Xu Z, Huang J, Lu J (2022) Hybrid handcrafted and learned feature framework for human action recognition. Appl Intell 52(11):12771–12787. https://doi.org/10.1007/s10489-021-03068-w
3. Yao G, Lei T, Zhong J, Jiang P (2019) Learning multi-temporal-scale deep information for action recognition. Appl Intell 49(6):2017–2029. https://doi.org/10.1007/s10489-018-1347-3
4. Jiang G, Jiang X, Fang Z, Chen S (2021) An efficient attention module for 3d convolutional neural networks in action recognition. Appl Intell 51(10):7043–7057. https://doi.org/10.1007/s10489-021-02195-8
5. Carreira J, Zisserman A Quo vadis, action recognition? a new model and the kinetics dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4724–4733. https://doi.org/10.1109/CVPR.2017.502
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