Detection Transformer (DETR) is a Transformer architecture based object detection model. In this paper, we demonstrate that it can also be used as a data augmenter. We term our approach as DETR assisted CutMix, or DeMix for short. DeMix builds on CutMix, a simple yet highly effective data augmentation technique that has gained popularity in recent years. CutMix improves model performance by cutting and pasting a patch from one image onto another, yielding a new image. The corresponding label for this new example is specified as the weighted average of the original labels, where the weight is proportional to the area of the patch. CutMix selects a random patch to be cut. In contrast, DeMix elaborately selects a semantically rich patch, located by a pre-trained DETR. The label of the new image is specified in the same way as in CutMix. Experimental results on benchmark datasets for image classification demonstrate that DeMix significantly outperforms prior art data augmentation methods including CutMix. Oue code is available at https://github.com/ZJLAB-AMMI/DeMix.

翻译：检测Transformer（DETR）是一种基于Transformer架构的目标检测模型。本文证明，它同样可用作数据增强器。我们将该方法称为DETR辅助的CutMix，简称DeMix。DeMix基于CutMix构建，后者是一种简单但高效的数据增强技术，近年来广受欢迎。CutMix通过将一张图像中的某个区域裁剪并粘贴到另一张图像上生成新图像，从而提升模型性能。新样本的对应标签被定义为原始标签的加权平均值，权重与裁剪区域的面积成正比。CutMix随机选择待裁剪区域，而DeMix则精心选择由预训练DETR定位的语义丰富区域。新图像的标签定义方式与CutMix相同。在图像分类基准数据集上的实验结果表明，DeMix显著优于包括CutMix在内的现有数据增强方法。我们的代码已开源：https://github.com/ZJLAB-AMMI/DeMix。