论文标题
metaage:元学习个性化年龄估计器
MetaAge: Meta-Learning Personalized Age Estimators
论文作者
论文摘要
不同的人以不同的方式衰老。为每个人学习个性化的年龄估计器是对年龄估计的有希望的方向,因为它可以更好地模拟衰老过程的个性化。但是,由于高级要求,大多数现有的个性化方法都缺乏大规模数据集:身份标签和足够的样本使每个人形成长期老化模式。在本文中,我们旨在学习没有上述要求的个性化年龄估计器,并提出一种称为年龄估计的元学习方法。与大多数现有的个性化方法不同,这些方法学习了培训集中每个人的个性化估计器的参数,我们的方法将映射从身份信息到年龄估计器参数。具体来说,我们引入了个性化的估算器元学习器,该估算值将身份功能作为输入并输出自定义估算器的参数。通过这种方式,我们的方法在没有上述要求的情况下学习了元知识,并将学习的元知识无缝转移到测试集中,这使我们能够利用现有的大规模年龄数据集,而无需任何其他注释。在包括Morph II,Chalearn Lap 2015和Chalearn Lap 2016数据库在内的三个基准数据集上进行的大量实验结果表明,我们的元大大提高了现有的个性化方法的性能,并优于最先进的方法。
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods suffer from the lack of large-scale datasets due to the high-level requirements: identity labels and enough samples for each person to form a long-term aging pattern. In this paper, we aim to learn personalized age estimators without the above requirements and propose a meta-learning method named MetaAge for age estimation. Unlike most existing personalized methods that learn the parameters of a personalized estimator for each person in the training set, our method learns the mapping from identity information to age estimator parameters. Specifically, we introduce a personalized estimator meta-learner, which takes identity features as the input and outputs the parameters of customized estimators. In this way, our method learns the meta knowledge without the above requirements and seamlessly transfers the learned meta knowledge to the test set, which enables us to leverage the existing large-scale age datasets without any additional annotations. Extensive experimental results on three benchmark datasets including MORPH II, ChaLearn LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge significantly boosts the performance of existing personalized methods and outperforms the state-of-the-art approaches.