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.

在长尾场景下，数据为模型提供了强大的需求，数据中心方法旨在增强数据的数量和质量以提高模型性能。本研究从特征多样性和分布转移的角度解决该问题，引入了特征多样性增益（FDG）的概念来阐明信息增强的有效性，实验证明使用FDG选择扩增数据可以进一步提高模型性能，而无需对模型的体系结构进行任何修改。因此，数据中心方法在长尾识别领域具有重要的潜力，超越了新模型结构的开发。此外，我们首次系统介绍了数据中心长尾学习框架的核心组件和基本任务，这些核心组件指导系统的实施和部署，而相应的基本任务则完善和扩展研究领域。

以数据为中心的长尾图像识别