  Crop-based training strategies decouple training resolution from GPU memory
consumption, allowing the use of large-capacity panoptic segmentation networks
on multi-megapixel images. Using crops, however, can introduce a bias towards
truncating or missing large objects. To address this, we propose a novel
crop-aware bounding box regression loss (CABB loss), which promotes predictions
to be consistent with the visible parts of the cropped objects, while not
over-penalizing them for extending outside of the crop. We further introduce a
novel data sampling and augmentation strategy which improves generalization
across scales by counteracting the imbalanced distribution of object sizes.
Combining these two contributions with a carefully designed, top-down panoptic
segmentation architecture, we obtain new state-of-the-art results on the
challenging Mapillary Vistas (MVD), Indian Driving and Cityscapes datasets,
surpassing the previously best approach on MVD by +4.5% PQ and +5.2% mAP.
