  Facial age estimation is an important yet very challenging problem in
computer vision. To improve the performance of facial age estimation, we first
formulate a simple standard baseline and build a much strong one by collecting
the tricks in pre-training, data augmentation, model architecture, and so on.
Compared with the standard baseline, the proposed one significantly decreases
the estimation errors. Moreover, long-tailed recognition has been an important
topic in facial age datasets, where the samples often lack on the elderly and
children. To train a balanced age estimator, we propose a two-stage training
method named Long-tailed Age Estimation (LAE), which decouples the learning
procedure into representation learning and classification. The effectiveness of
our approach has been demonstrated on the dataset provided by organizers of
Guess The Age Contest 2021.
