The success of CutMix in image classification has sparked interest in saliency-based mix augmentation methods, which refer to detecting saliency regions to generate more valid images. However, existing mix works either require external tools to locate saliency regions, or rely on additional complex optimization policy for generating new images, which limits their application ranges. To address these deficiencies, we propose Gradient Saliency-based Mix (GradSalMix), a simple yet more general mix augmentation, whose operations are all based on the gradients of the training neural network itself. Specifically, we first locate the saliency regions of two images via their gradients of manifolds, and then directly migrate the region, sampled around the center with a large gradient response value, from one image to another. Afterwards, the labels of images are weighted by their accumulated gradient values for new soft labels, which are shown more accurate than the ones weighted by area ratio. The experimental results show that our proposed method outperforms previous works, in terms of accuracy and robustness against adversarial attacks, on four image classification benchmarks. Moreover, extensive experiments on object detection and point cloud classification also verify the superiority and generality of our method.