  Prevalent state-of-the-art instance segmentation methods fall into a
query-based scheme, in which instance masks are derived by querying the image
feature using a set of instance-aware embeddings. In this work, we devise a new
training framework that boosts query-based models through discriminative query
embedding learning. It explores two essential properties, namely dataset-level
uniqueness and transformation equivariance, of the relation between queries and
instances. First, our algorithm uses the queries to retrieve the corresponding
instances from the whole training dataset, instead of only searching within
individual scenes. As querying instances across scenes is more challenging, the
segmenters are forced to learn more discriminative queries for effective
instance separation. Second, our algorithm encourages both image (instance)
representations and queries to be equivariant against geometric
transformations, leading to more robust, instance-query matching. On top of
four famous, query-based models ($i.e.,$ CondInst, SOLOv2, SOTR, and
Mask2Former), our training algorithm provides significant performance gains
($e.g.,$ +1.6 - 3.2 AP) on COCO dataset. In addition, our algorithm promotes
the performance of SOLOv2 by 2.7 AP, on LVISv1 dataset.
