Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks. While various strategies have been proposed, Mixed Sample Data Augmentation (MSDA) has shown great potential for enhancing model performance and generalization. We introduce a novel mixup method called MiAMix, which stands for Multi-stage Augmented Mixup. MiAMix integrates image augmentation into the mixup framework, utilizes multiple diversified mixing methods concurrently, and improves the mixing method by randomly selecting mixing mask augmentation methods. Recent methods utilize saliency information and the MiAMix is designed for computational efficiency as well, reducing additional overhead and offering easy integration into existing training pipelines. We comprehensively evaluate MiaMix using four image benchmarks and pitting it against current state-of-the-art mixed sample data augmentation techniques to demonstrate that MIAMix improves performance without heavy computational overhead.

翻译：尽管深度学习领域取得了显著进展，过拟合仍然是一个关键挑战，而数据增强因其在各类计算机视觉任务中提升模型泛化能力的效果，已成为一种极具前景的方法。尽管已有多种策略被提出，混合样本数据增强（MSDA）在提升模型性能与泛化能力方面展现出巨大潜力。我们提出了一种名为MiAMix的新型混合方法，其全称为多阶段增强混合（Multi-stage Augmented Mixup）。MiAMix将图像增强集成到混合框架中，同时利用多种多样化的混合方法，并通过随机选择混合掩膜增强方法来改进混合过程。近期方法利用显著性信息，而MiAMix在设计时也兼顾了计算效率，减少了额外开销，并易于集成到现有训练流程中。我们使用四个图像基准测试集，将MiAMix与当前最先进的混合样本数据增强技术进行全面对比评估，结果表明MiAMix在不产生高计算开销的前提下提升了性能。