  Most self-supervised learning (SSL) methods often work on curated datasets
where the object-centric assumption holds. This assumption breaks down in
uncurated images. Existing scene image SSL methods try to find the two views
from original scene images that are well matched or dense, which is both
complex and computationally heavy. This paper proposes a conceptually different
pipeline: first find regions that are coarse objects (with adequate
objectness), crop them out as pseudo object-centric images, then any SSL method
can be directly applied as in a real object-centric dataset. That is, coarse
crops benefits scene images SSL. A novel cropping strategy that produces coarse
object box is proposed. The new pipeline and cropping strategy successfully
learn quality features from uncurated datasets without ImageNet. Experiments
show that our pipeline outperforms existing SSL methods (MoCo-v2, DenseCL and
MAE) on classification, detection and segmentation tasks. We further conduct
extensively ablations to verify that: 1) the pipeline do not rely on pretrained
models; 2) the cropping strategy is better than existing object discovery
methods; 3) our method is not sensitive to hyperparameters and data
augmentations.

Computer Vision and Pattern Recognition