  For the semantic segmentation of images, state-of-the-art deep neural
networks (DNNs) achieve high segmentation accuracy if that task is restricted
to a closed set of classes. However, as of now DNNs have limited ability to
operate in an open world, where they are tasked to identify pixels belonging to
unknown objects and eventually to learn novel classes, incrementally. Humans
have the capability to say: I don't know what that is, but I've already seen
something like that. Therefore, it is desirable to perform such an incremental
learning task in an unsupervised fashion. We introduce a method where unknown
objects are clustered based on visual similarity. Those clusters are utilized
to define new classes and serve as training data for unsupervised incremental
learning. More precisely, the connected components of a predicted semantic
segmentation are assessed by a segmentation quality estimate. connected
components with a low estimated prediction quality are candidates for a
subsequent clustering. Additionally, the component-wise quality assessment
allows for obtaining predicted segmentation masks for the image regions
potentially containing unknown objects. The respective pixels of such masks are
pseudo-labeled and afterwards used for re-training the DNN, i.e., without the
use of ground truth generated by humans. In our experiments we demonstrate
that, without access to ground truth and even with few data, a DNN's class
space can be extended by a novel class, achieving considerable segmentation
accuracy.
