Lightweight facial landmark detection network based on improved MobileViT
Author:
Funder
the Program for Inno-vative Research Team in University of Tianjin
the Tianjin Science and Technology Popularization Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02975-4.pdf
Reference21 articles.
1. Xiong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. IEEE Conf. Comput. Vis. Pattern Recognit. (2013). https://doi.org/10.1109/CVPR.2013.75
2. Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107, 177–190 (2014). https://doi.org/10.1007/s11263-013-0667-3
3. Sun, Y., Xiaogang, W., Xiaoou, T.: Deep convolutional network cascade for facial point detection. IEEE Conf. Comput. Vis. Pattern Recognit. (2013). https://doi.org/10.1109/CVPR.2013.446
4. Wu, W., Qian, C., Yang, S., Wang, Q., Cai, Y., Zhou, Q.: Look at boundary: a boundary-aware face alignment algorithm. IEEE Conf. Comput. Vis. Pattern Recognit. (2018). https://doi.org/10.1109/CVPR.2018.00227
5. Kumar, A., Chellappa, R.: Disentangling 3D pose in a dendritic CNN for unconstrained 2D face alignment. IEEE Conf. Comput. Vis. Pattern Recognit. (2018). https://doi.org/10.1109/CVPR.2018.00052
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