  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.
