  Dense image segmentation tasks e.g., semantic, panoptic) are useful for image
editing, but existing methods can hardly generalize well in an in-the-wild
setting where there are unrestricted image domains, classes, and image
resolution and quality variations. Motivated by these observations, we
construct a new entity segmentation dataset, with a strong focus on
high-quality dense segmentation in the wild. The dataset contains images
spanning diverse image domains and entities, along with plentiful
high-resolution images and high-quality mask annotations for training and
testing. Given the high-quality and -resolution nature of the dataset, we
propose CropFormer which is designed to tackle the intractability of
instance-level segmentation on high-resolution images. It improves mask
prediction by fusing high-res image crops that provide more fine-grained image
details and the full image. CropFormer is the first query-based Transformer
architecture that can effectively fuse mask predictions from multiple image
views, by learning queries that effectively associate the same entities across
the full image and its crop. With CropFormer, we achieve a significant AP gain
of $1.9$ on the challenging entity segmentation task. Furthermore, CropFormer
consistently improves the accuracy of traditional segmentation tasks and
datasets. The dataset and code will be released at
http://luqi.info/entityv2.github.io/.
