  Data augmentation is a series of techniques that generate high-quality
artificial data by manipulating existing data samples. By leveraging data
augmentation techniques, AI models can achieve significantly improved
applicability in tasks involving scarce or imbalanced datasets, thereby
substantially enhancing AI models' generalization capabilities. Existing
literature surveys only focus on a certain type of specific modality data, and
categorize these methods from modality-specific and operation-centric
perspectives, which lacks a consistent summary of data augmentation methods
across multiple modalities and limits the comprehension of how existing data
samples serve the data augmentation process. To bridge this gap, we propose a
more enlightening taxonomy that encompasses data augmentation techniques for
different common data modalities. Specifically, from a data-centric
perspective, this survey proposes a modality-independent taxonomy by
investigating how to take advantage of the intrinsic relationship between data
samples, including single-wise, pair-wise, and population-wise sample data
augmentation methods. Additionally, we categorize data augmentation methods
across five data modalities through a unified inductive approach.
