  In this paper, we propose a new data augmentation strategy named Thumbnail,
which aims to strengthen the network's capture of global features. We get a
generated image by reducing an image to a certain size, which is called as the
thumbnail, and pasting it in the random position of the original image. The
generated image not only retains most of the original image information but
also has the global information in the thumbnail. Furthermore, we find that the
idea of thumbnail can be perfectly integrated with Mixed Sample Data
Augmentation, so we paste the thumbnail in another image where the ground truth
labels are also mixed with a certain weight, which makes great achievements on
various computer vision tasks. Extensive experiments show that Thumbnail works
better than the state-of-the-art augmentation strategies across classification,
fine-grained image classification, and object detection. On ImageNet
classification, ResNet50 architecture with our method achieves 79.21% accuracy,
which is more than 2.89% improvement on the baseline.
