  Instance segmentation is a computer vision task where separate objects in an
image are detected and segmented. State-of-the-art deep neural network models
require large amounts of labeled data in order to perform well in this task.
Making these annotations is time-consuming. We propose for the first time, an
iterative learning and annotation method that is able to detect, segment and
annotate instances in datasets composed of multiple similar objects. The
approach requires minimal human intervention and needs only a bootstrapping set
containing very few annotations. Experiments on two different datasets show the
validity of the approach in different applications related to visual
inspection.
