Uncertainty-oriented Order Learning for Facial Beauty Prediction
<|reference_start|>Uncertainty-oriented Order Learning for Facial Beauty Prediction: Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability.<|reference_end|>
@article{liang2024uncertainty-oriented,
    title={Uncertainty-oriented Order Learning for Facial Beauty Prediction},
    author={Xuefeng Liang, Zhenyou Liu, Jian Lin, Xiaohui Yang and Takatsune
  Kumada},
    journal={arXiv preprint arXiv:2409.00603},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00603},
    primaryClass={cs.CV}
}