Zusammenfassung
We introduce a new image segmentation task, termed Entity Segmentation (ES)
with the aim to segment all visual entities in an image without considering
semantic category labels. It has many practical applications in image
manipulation/editing where the segmentation mask quality is typically crucial
but category labels are less important. In this setting, all
semantically-meaningful segments are equally treated as categoryless entities
and there is no thing-stuff distinction. Based on our unified entity
representation, we propose a center-based entity segmentation framework with
two novel modules to improve mask quality. Experimentally, both our new task
and framework demonstrate superior advantages as against existing work. In
particular, ES enables the following: (1) merging multiple datasets to form a
large training set without the need to resolve label conflicts; (2) any model
trained on one dataset can generalize exceptionally well to other datasets with
unseen domains. Our code is made publicly available at
https://github.com/dvlab-research/Entity.
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