This paper addresses the instance segmentation task in the open-set conditions, where input images might contain known and unknown object classes. Existing instance segmentation methods require annotating masks for all object instances, which is expensive and even infeasible in realistic scenarios, where the number of categories increases boundlessly. In this paper, we present a novel open-set instance segmentation approach to segment all object instances in images. Based on the output of an object detector/segmenter trained on known classes, we seek a global image segmentation, whose regions are each assigned to one of the known or unknown classes. We formulate the problem in a Bayesian framework, and approximate the posterior distribution using a simulated annealing optimization equipped with an efficient image partition sampler. We show empirically that our method is competitive with state-of-the-art supervised methods on known classes, but also performs well on unknown classes when compared with unsupervised methods.

本文提出了一种基于贝叶斯框架和模拟退火优化的图像划分采样算法，基于已知物体类别的目标检测器输出，实现了在开放集条件下分割所有已知和未知目标类别的实例分割方法，该方法在已知类别和未知类别上均有较好表现并且与监督式方法相比具有竞争性。

基于贝叶斯的开放世界语义实例分割