Refine and Represent: Region-to-Object Representation Learning
This addresses the challenge of improving dense prediction tasks like segmentation for computer vision researchers, though it appears incremental as it builds on existing region-based and object-centric methods.
The paper tackles the problem of unifying region-based and object-centric pretraining in self-supervised learning by introducing Region-to-Object Representation Learning (R2O), which refines regions into object masks and learns joint representations, resulting in state-of-the-art performance with improvements like +0.7 mIOU on PASCAL VOC and +2.9 mIoU on Caltech-UCSD Birds.
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.