Facial age estimation has received a lot of attention for its diverse application scenarios. Most existing studies treat each sample equally and aim to reduce the average estimation error for the entire dataset, which can be summarized as General Age Estimation. However, due to the long-tailed distribution prevalent in the dataset, treating all samples equally will inevitably bias the model toward the head classes (usually the adult with a majority of samples). Driven by this, some works suggest that each class should be treated equally to improve performance in tail classes (with a minority of samples), which can be summarized as Long-tailed Age Estimation. However, Long-tailed Age Estimation usually faces a performance trade-off, i.e., achieving improvement in tail classes by sacrificing the head classes. In this paper, our goal is to design a unified framework to perform well on both tasks, killing two birds with one stone. To this end, we propose a simple, effective, and flexible training paradigm named GLAE, which is two-fold. Our GLAE provides a surprising improvement on Morph II, reaching the lowest MAE and CMAE of 1.14 and 1.27 years, respectively. Compared to the previous best method, MAE dropped by up to 34%, which is an unprecedented improvement, and for the first time, MAE is close to 1 year old. Extensive experiments on other age benchmark datasets, including CACD, MIVIA, and Chalearn LAP 2015, also indicate that GLAE outperforms the state-of-the-art approaches significantly.

翻译：面部年龄估计因其多样化的应用场景而受到广泛关注。现有研究大多平等对待每个样本，旨在降低整个数据集的平均估计误差，这可以概括为通用年龄估计。然而，由于数据集中普遍存在的长尾分布，平等对待所有样本不可避免地会使模型偏向头部类别（通常是样本占多数的成人）。受此驱动，一些工作建议平等对待每个类别，以提升尾部类别（样本占少数的类别）的性能，这可以概括为长尾年龄估计。然而，长尾年龄估计通常面临性能权衡，即通过牺牲头部类别来实现尾部类别的改进。本文旨在设计一个统一框架，在两项任务上均表现出色，实现一石二鸟。为此，我们提出了一种简单、有效且灵活的训练范式，命名为GLAE，它包含两个方面。我们的GLAE在Morph II数据集上实现了惊人的改进，分别达到最低的MAE和CMAE，为1.14年和1.27年。与之前的最优方法相比，MAE下降了高达34%，这是前所未有的改进，并且首次使MAE接近1岁。在其他年龄基准数据集（包括CACD、MIVIA和Chalearn LAP 2015）上的大量实验也表明，GLAE显著优于现有最先进的方法。