Decision-level information fusion powered human pose estimation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03623-z.pdf
Reference49 articles.
1. Chen Y, Tian Y, He M (2020) Monocular human pose estimation: A survey of deep learning-based methods. Comput Vis Image Underst 192. https://doi.org/10.1016/j.cviu.2019.102897
2. Luvizon D, Picard D, Tabia H (2020) Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action Recognition. IEEE Trans Pattern Anal Mach Intell:1–1. https://doi.org/10.1109/TPAMI.2020.2976014
3. Sun Y, Huang H, Yun X, Yang B, Dong K (2021) Triplet attention multiple spacetime-semantic graph convolutional network for skeleton-based action recognition. Appl Intell. https://doi.org/10.1007/s10489-021-02370-x
4. Yoon Y, Yu J, Jeon M (2021) Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition. Appl Intell. https://doi.org/10.1007/s10489-021-02487-z
5. Gao C, Chen Y, Yu J-G, Sang N (2020) Pose-guided spatiotemporal alignment for video-based person Re-identification. Inf Sci 527:176–190. https://doi.org/10.1016/j.ins.2020.04.007
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