  Data augmentation is a powerful technique to increase the diversity of data,
which can effectively improve the generalization ability of neural networks in
image recognition tasks. Recent data mixing based augmentation strategies have
achieved great success. Especially, CutMix uses a simple but effective method
to improve the classifiers by randomly cropping a patch from one image and
pasting it on another image. To further promote the performance of CutMix, a
series of works explore to use the saliency information of the image to guide
the mixing. We systematically study the importance of the saliency information
for mixing data, and find that the saliency information is not so necessary for
promoting the augmentation performance. Furthermore, we find that the cutting
based data mixing methods carry two problems of label misallocation and object
information missing, which cannot be resolved simultaneously. We propose a more
effective but very easily implemented method, namely ResizeMix. We mix the data
by directly resizing the source image to a small patch and paste it on another
image. The obtained patch preserves more substantial object information
compared with conventional cut-based methods. ResizeMix shows evident
advantages over CutMix and the saliency-guided methods on both image
classification and object detection tasks without additional computation cost,
which even outperforms most costly search-based automatic augmentation methods.
