  Deep learning has significantly improved the precision of instance
segmentation with abundant labeled data. However, in many areas like medical
and manufacturing, collecting sufficient data is extremely hard and labeling
this data requires high professional skills. We follow this motivation and
propose a new task set named zero-shot instance segmentation (ZSI). In the
training phase of ZSI, the model is trained with seen data, while in the
testing phase, it is used to segment all seen and unseen instances. We first
formulate the ZSI task and propose a method to tackle the challenge, which
consists of Zero-shot Detector, Semantic Mask Head, Background Aware RPN and
Synchronized Background Strategy. We present a new benchmark for zero-shot
instance segmentation based on the MS-COCO dataset. The extensive empirical
results in this benchmark show that our method not only surpasses the
state-of-the-art results in zero-shot object detection task but also achieves
promising performance on ZSI. Our approach will serve as a solid baseline and
facilitate future research in zero-shot instance segmentation.
