  Self-supervised methods (SSL) have achieved significant success via
maximizing the mutual information between two augmented views, where cropping
is a popular augmentation technique. Cropped regions are widely used to
construct positive pairs, while the left regions after cropping have rarely
been explored in existing methods, although they together constitute the same
image instance and both contribute to the description of the category. In this
paper, we make the first attempt to demonstrate the importance of both regions
in cropping from a complete perspective and propose a simple yet effective
pretext task called Region Contrastive Learning (RegionCL). Specifically, given
two different images, we randomly crop a region (called the paste view) from
each image with the same size and swap them to compose two new images together
with the left regions (called the canvas view), respectively. Then, contrastive
pairs can be efficiently constructed according to the following simple
criteria, i.e., each view is (1) positive with views augmented from the same
original image and (2) negative with views augmented from other images. With
minor modifications to popular SSL methods, RegionCL exploits those abundant
pairs and helps the model distinguish the regions features from both canvas and
paste views, therefore learning better visual representations. Experiments on
ImageNet, MS COCO, and Cityscapes demonstrate that RegionCL improves MoCo v2,
DenseCL, and SimSiam by large margins and achieves state-of-the-art performance
on classification, detection, and segmentation tasks. The code will be
available at https://github.com/Annbless/RegionCL.git.
