  Open-World Instance Segmentation (OWIS) is an emerging research topic that
aims to segment class-agnostic object instances from images. The mainstream
approaches use a two-stage segmentation framework, which first locates the
candidate object bounding boxes and then performs instance segmentation. In
this work, we instead promote a single-stage framework for OWIS. We argue that
the end-to-end training process in the single-stage framework can be more
convenient for directly regularizing the localization of class-agnostic object
pixels. Based on the single-stage instance segmentation framework, we propose a
regularization model to predict foreground pixels and use its relation to
instance segmentation to construct a cross-task consistency loss. We show that
such a consistency loss could alleviate the problem of incomplete instance
annotation -- a common problem in the existing OWIS datasets. We also show that
the proposed loss lends itself to an effective solution to semi-supervised OWIS
that could be considered an extreme case that all object annotations are absent
for some images. Our extensive experiments demonstrate that the proposed method
achieves impressive results in both fully-supervised and semi-supervised
settings. Compared to SOTA methods, the proposed method significantly improves
the $AP_{100}$ score by 4.75\% in UVO$\rightarrow$UVO setting and 4.05\% in
COCO$\rightarrow$UVO setting. In the case of semi-supervised learning, our
model learned with only 30\% labeled data, even outperforms its
fully-supervised counterpart with 50\% labeled data. The code will be released
soon.
