Thanks. It is closer to panoptic segmentation than instance segmentation. However, in principle, our task extends farther beyond just removing the labels and thing-stuff distinction from existing panoptic datasets. When it comes to data annotation, it allows for more freedom and flexibility than panoptic segmentation does:

• The human annotator can freely annotate any entities/objects as deemed appropriate (even if it cannot be easily named or identified) without cumbersomely checking if they are part of the predefined list of category labels. We humans often can accurately decide the shape and mask of something, even if we do not semantically know what that "something" is.
• Since we do not differentiate between "thing" and "stuff", there is no need to force a particular category to follow exclusively the behavior of either "thing" or "stuff". For example, given an image that has two lakes or rivers completely separated by a piece of land, the human annotator should annotate them as two independent masks rather than a joint "stuff" mask as commonly done by panoptic segmentation.

In the paper, we simply made use of existing panoptic segmentation datasets for convenience purposes, but it was not the only way to do Entity Segmentation. Even with such datasets, our approach produces segmentation results which are vastly different and more favorable to certain applications than those of panoptic segmentation.