  Recent works in self-supervised learning have demonstrated strong performance
on scene-level dense prediction tasks by pretraining with object-centric or
region-based correspondence objectives. In this paper, we present
Region-to-Object Representation Learning (R2O) which unifies region-based and
object-centric pretraining. R2O operates by training an encoder to dynamically
refine region-based segments into object-centric masks and then jointly learns
representations of the contents within the mask. R2O uses a "region refinement
module" to group small image regions, generated using a region-level prior,
into larger regions which tend to correspond to objects by clustering
region-level features. As pretraining progresses, R2O follows a
region-to-object curriculum which encourages learning region-level features
early on and gradually progresses to train object-centric representations.
Representations learned using R2O lead to state-of-the art performance in
semantic segmentation for PASCAL VOC (+0.7 mIOU) and Cityscapes (+0.4 mIOU) and
instance segmentation on MS COCO (+0.3 mask AP). Further, after pretraining on
ImageNet, R2O pretrained models are able to surpass existing state-of-the-art
in unsupervised object segmentation on the Caltech-UCSD Birds 200-2011 dataset
(+2.9 mIoU) without any further training. We provide the code/models from this
work at https://github.com/KKallidromitis/r2o.
