  Unified panoptic segmentation methods are achieving state-of-the-art results
on several datasets. To achieve these results on high-resolution datasets,
these methods apply crop-based training. In this work, we find that, although
crop-based training is advantageous in general, it also has a harmful
side-effect. Specifically, it limits the ability of unified networks to
discriminate between large object instances, causing them to make predictions
that are confused between multiple instances. To solve this, we propose
Intra-Batch Supervision (IBS), which improves a network's ability to
discriminate between instances by introducing additional supervision using
multiple images from the same batch. We show that, with our IBS, we
successfully address the confusion problem and consistently improve the
performance of unified networks. For the high-resolution Cityscapes and
Mapillary Vistas datasets, we achieve improvements of up to +2.5 on the
Panoptic Quality for thing classes, and even more considerable gains of up to
+5.8 on both the pixel accuracy and pixel precision, which we identify as
better metrics to capture the confusion problem.
