  Many objects do not appear frequently enough in complex scenes (e.g., certain
handbags in living rooms) for training an accurate object detector, but are
often found frequently by themselves (e.g., in product images). Yet, these
object-centric images are not effectively leveraged for improving object
detection in scene-centric images. In this paper, we propose Mosaic of
Object-centric images as Scene-centric images (MosaicOS), a simple and novel
framework that is surprisingly effective at tackling the challenges of
long-tailed object detection. Keys to our approach are three-fold: (i) pseudo
scene-centric image construction from object-centric images for mitigating
domain differences, (ii) high-quality bounding box imputation using the
object-centric images' class labels, and (iii) a multi-stage training
procedure. On LVIS object detection (and instance segmentation), MosaicOS leads
to a massive 60% (and 23%) relative improvement in average precision for rare
object categories. We also show that our framework can be compatibly used with
other existing approaches to achieve even further gains. Our pre-trained models
are publicly available at https://github.com/czhang0528/MosaicOS/.
