Debugging Human Pose Estimation with Explainable AI
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5504-2_86
Reference12 articles.
1. Duan, C., Hu, B., Liu, W., Song, J.: Motion capture for sporting events based on graph convolutional neural networks and single target pose estimation algorithms. Appl. Sci. 13, 7611 (2023). https://doi.org/10.3390/app13137611
2. Zheng, C., Wenhan, W., Chen, C., Yang, T., Sijie Zhu, J., Shen, N. K., Shah, M.: Deep learning-based human pose estimation: a survey. ACM Comput. Surv. 56(1), 1–37 (2023). https://doi.org/10.1145/3603618
3. Dong, F., Yang, M., Yang, P.: Human pose estimation based on HRNet and feature pyramids. Highl. Sci. Eng. Technol. 39, 1239–1244 (2023). https://doi.org/10.54097/hset.v39i.6734
4. Zhang, S., Qiang, B., Yang, X., Wei, X., Chen, R., Chen, L.: Human pose estimation via an ultra-lightweight pose distillation network. Electronics 12, 2593 (2023). https://doi.org/10.3390/electronics12122593
5. You, B., Sun, M., Li, J.: High-accuracy pose estimation algorithm with monocular camera based on residual network. OE. 62, 054102 (2023). https://doi.org/10.1117/1.OE.62.5.054102
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