Zero-shot instance segmentation aims to detect and precisely segment objects
of unseen categories without any training samples. Since the model is trained
on seen categories, there is a strong bias that the model tends to classify all
the objects into seen categories. Besides, there is a natural confusion between
background and novel objects that have never shown up in training. These two
challenges make novel objects hard to be raised in the final instance
segmentation results. It is desired to rescue novel objects from background and
dominated seen categories. To this end, we propose D$^2$Zero with
Semantic-Promoted Debiasing and Background Disambiguation to enhance the
performance of Zero-shot instance segmentation. Semantic-promoted debiasing
utilizes inter-class semantic relationships to involve unseen categories in
visual feature training and learns an input-conditional classifier to conduct
dynamical classification based on the input image. Background disambiguation
produces image-adaptive background representation to avoid mistaking novel
objects for background. Extensive experiments show that we significantly
outperform previous state-of-the-art methods by a large margin, e.g., 16.86%
improvement on COCO. Project page: this https URL

本文提出 D$^2$Zero 模型结合语义推动减偏和背景消除，以提高零样本实例分割人工智能的表现，实验表明我们的方法比先前的最先进方法显着优越。