用不确定性建模解决人脸美丑预测中的标准不一致问题
Uncertainty-oriented Order Learning for Facial Beauty Prediction
- 通过学习人脸间的美丑排序关系替代直接打分
- 在五个数据集上准确率与泛化能力均优于现有方法
- 适合关注人脸审美主观差异的研究者
以往的人脸美丑预测方法通常将图像的美丑特征建模为潜在空间中的一个点，并学习从该点到精确分数的映射。尽管现有回归方法在单一数据集上表现良好，但对测试数据敏感且泛化能力弱。我们认为这些方法低估了人脸美丑预测中存在的两类不一致性：1. 多个数据集间美丑标准的差异；2. 人类对同一张人脸美丑认知的差异。为此，我们提出一种新的不确定性导向排序学习（Uncertainty-oriented Order Learning, UOL），其中排序学习通过学习人脸间的美丑顺序关系来应对标准不一致问题，而不确定性建模则表征人类认知的差异性。UOL的关键贡献是一个设计的分布比较模块，使传统排序学习能够处理不确定数据。在五个数据集上的大量实验表明，UOL在准确率和泛化能力方面均优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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