  This paper addresses the semantic instance segmentation task in the open-set
conditions, where input images can contain known and unknown object classes.
The training process of existing semantic instance segmentation methods
requires annotation masks for all object instances, which is expensive to
acquire or even infeasible in some realistic scenarios, where the number of
categories may increase boundlessly. In this paper, we present a novel open-set
semantic instance segmentation approach capable of segmenting all known and
unknown object classes in images, based on the output of an object detector
trained on known object classes. We formulate the problem using a Bayesian
framework, where the posterior distribution is approximated with 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.

Computer Vision and Pattern Recognition