Dynamic Attentive Convolution for Facial Beauty Prediction
Author:
Affiliation:
1. College of Computer and Data Science, Fuzhou University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/2/E107.D_2023EDL8058/_pdf
Reference28 articles.
1. [1] D. Zhang, F. Chen, and Y. Xu, Computer Models for Facial Beauty Analysis, Springer, 2016.
2. [2] L. Liang, L. Lin, L. Jin, D. Xie, and M. Li, “Scut-fbp5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction,” ICPR, pp.1598-1603, IEEE, 2018. 10.1109/icpr.2018.8546038
3. [3] L. Lin, L. Liang, and L. Jin, “R 2-resnext: A resnext-based regression model with relative ranking for facial beauty prediction,” ICPR, pp.85-90, IEEE, 2018. 10.1109/icpr.2018.8545164
4. [4] L. Lin, L. Liang, L. Jin, and W. Chen, “Attribute-aware convolutional neural networks for facial beauty prediction.,” IJCAI, pp.847-853, 2019. 10.24963/ijcai.2019/119
5. [5] L. Liu, J. Xing, S. Liu, H. Xu, X. Zhou, and S. Yan, “Wow! you are so beautiful today!,” TOMM, vol.11, no.1s, pp.1-22, 2014. 10.1145/2659234
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