  In the context of the long-tail scenario, models exhibit a strong demand for
high-quality data. Data-centric approaches aim to enhance both the quantity and
quality of data to improve model performance. Among these approaches,
information augmentation has been progressively introduced as a crucial
category. It achieves a balance in model performance by augmenting the richness
and quantity of samples in the tail classes. However, there is currently a lack
of research into the underlying mechanisms explaining the effectiveness of
information augmentation methods. Consequently, the utilization of information
augmentation in long-tail recognition tasks relies heavily on empirical and
intricate fine-tuning. This work makes two primary contributions. Firstly, we
approach the problem from the perspectives of feature diversity and
distribution shift, introducing the concept of Feature Diversity Gain (FDG) to
elucidate why information augmentation is effective. We find that the
performance of information augmentation can be explained by FDG, and its
performance peaks when FDG achieves an appropriate balance. Experimental
results demonstrate that by using FDG to select augmented data, we can further
enhance model performance without the need for any modifications to the model's
architecture. Thus, data-centric approaches hold significant potential in the
field of long-tail recognition, beyond the development of new model structures.
Furthermore, we systematically introduce the core components and fundamental
tasks of a data-centric long-tail learning framework for the first time. These
core components guide the implementation and deployment of the system, while
the corresponding fundamental tasks refine and expand the research area.
