Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixing augmentation strategies have advanced to generate the mixup mask that can enrich the saliency information, which is a supervisory signal. However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. We develop an efficient pairing algorithm that pursues to minimize the conflict of salient regions of paired images and achieve rich saliency in mixup images. Moreover, GuidedMixup controls the mixup ratio for each pixel to better preserve the salient region by interpolating two paired images smoothly. The experiments on several datasets demonstrate that GuidedMixup provides a good trade-off between augmentation overhead and generalization performance on classification datasets. In addition, our method shows good performance in experiments with corrupted or reduced datasets.

翻译：数据增强如今已成为图像训练过程中不可或缺的一部分，因为它能有效防止过拟合并使模型对含噪声数据集更具鲁棒性。近期的混合增强策略已发展到能够生成富含显著性信息（一种监督信号）的混合掩码，然而这些方法在优化混合掩码时产生了显著的计算负担。基于这一动机，我们提出了一种新型显著性感知混合方法——GuidedMixup，旨在以较低的计算开销保留混合图像中的显著性区域。我们开发了一种高效配对算法，力求最小化配对图像显著性区域的冲突，并在混合图像中实现丰富的显著性信息。此外，GuidedMixup通过平滑插值两个配对图像，控制每个像素的混合比例，从而更好地保留显著性区域。在多个数据集上的实验表明，GuidedMixup在分类数据集的增强开销与泛化性能之间取得了良好的平衡。同时，我们的方法在含噪声或缩减数据集上的实验中也展现出优异的性能。