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.

通过提出 GLAE 方法，本文在多个年龄估计数据集上获得了显著的性能改进，实现了在头部和尾部样本类别上的优秀表现。

通用与长尾年龄估计：一石二鸟的方法

General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds  with One Stone

Deep discriminative models (e.g. deep regression forests, deep neural
decision forests) have achieved remarkable success recently to solve problems
such as facial age estimation and head pose estimation. Most existing methods
pursue robust and unbiased solutions either through learning discriminative
features, or reweighting samples. We argue what is more desirable is learning
gradually to discriminate like our human beings, and hence we resort to
self-paced learning (SPL). Then, a natural question arises: can self-paced
regime lead deep discriminative models to achieve more robust and less biased
solutions? To this end, this paper proposes a new deep discriminative
model--self-paced deep regression forests with consideration on
underrepresented examples (SPUDRFs). It tackles the fundamental ranking and
selecting problem in SPL from a new perspective: fairness. This paradigm is
fundamental and could be easily combined with a variety of deep discriminative
models (DDMs). Extensive experiments on two computer vision tasks, i.e., facial
age estimation and head pose estimation, demonstrate the efficacy of SPUDRFs,
where state-of-the-art performances are achieved.

本文提出了一种新的自我定步深度回归森林模型（SPUDRFs）解决深度识别模型中的排序和选择问题，在人脸年龄估计和头部姿势估计等视觉任务中实现了最先进的表现。

自适应深度回归森林在考虑样本不平衡情况下的应用

Self-Paced Deep Regression Forests with Consideration on  Underrepresented Examples

Automatic facial age estimation can be used in a wide range of real-world
applications. However, this process is challenging due to the randomness and
slowness of the aging process. Accordingly, in this paper, we propose a
comprehensive framework aimed at overcoming the challenges associated with
facial age estimation. First, we propose a novel age encoding method, referred
to as 'Soft-ranking', which encodes two important properties of facial age,
i.e., the ordinal property and the correlation between adjacent ages.
Therefore, Soft-ranking provides a richer supervision signal for training deep
models. Moreover, we also carefully analyze existing evaluation protocols for
age estimation, finding that the overlap in identity between the training and
testing sets affects the relative performance of different age encoding
methods. Finally, since existing face databases for age estimation are
generally small, deep models tend to suffer from an overfitting problem. To
address this issue, we propose a novel regularization strategy to encourage
deep models to learn more robust features from facial parts for age estimation
purposes. Extensive experiments indicate that the proposed techniques improve
the age estimation performance; moreover, we achieve state-of-the-art
performance on the three most popular age databases, $i.e.$, Morph II,
CLAP2015, and CLAP2016.

本文提出了一个综合性框架，旨在克服面部年龄估计所涉及的挑战。通过提出一种新的年龄编码方法 Soft-ranking，分析现有的评估协议，并提出一种新的正则化策略以解决深度模型过拟合问题来提高模型在 Morph II，CLAP2015 和 CLAP2016 数据库上的性能。