CutMix is a popular augmentation technique commonly used for training modern
convolutional and transformer vision networks. It was originally designed to
encourage Convolution Neural Networks (CNNs) to focus more on an image's global
context instead of local information, which greatly improves the performance of
CNNs. However, we found it to have limited benefits for transformer-based
architectures that naturally have a global receptive field. In this paper, we
propose a novel data augmentation technique TokenMix to improve the performance
of vision transformers. TokenMix mixes two images at token level via
partitioning the mixing region into multiple separated parts. Besides, we show
that the mixed learning target in CutMix, a linear combination of a pair of the
ground truth labels, might be inaccurate and sometimes counter-intuitive. To
obtain a more suitable target, we propose to assign the target score according
to the content-based neural activation maps of the two images from a
pre-trained teacher model, which does not need to have high performance. With
plenty of experiments on various vision transformer architectures, we show that
our proposed TokenMix helps vision transformers focus on the foreground area to
infer the classes and enhances their robustness to occlusion, with consistent
performance gains. Notably, we improve DeiT-T/S/B with +1% ImageNet top-1
accuracy. Besides, TokenMix enjoys longer training, which achieves 81.2% top-1
accuracy on ImageNet with DeiT-S trained for 400 epochs. Code is available at
this https URL

本文提出了一种名为 TokenMix 的数据增强技术，它在令牌级别上混合两个图像，通过将混合区域分区成多个独立的部分来实现，其适用于更好地训练视觉变换器，同时根据预训练的 teacher 模型的内容基础神经激活映射分配目标分数，增强了其稳健性和性能。