  Pixelwise annotation of image sequences can be very tedious for humans.
Interactive video object segmentation aims to utilize automatic methods to
speed up the process and reduce the workload of the annotators. Most
contemporary approaches rely on deep convolutional networks to collect and
process information from human annotations throughout the video. However, such
networks contain millions of parameters and need huge amounts of labeled
training data to avoid overfitting. Beyond that, label propagation is usually
executed as a series of frame-by-frame inference steps, which is difficult to
be parallelized and is thus time consuming. In this paper we present a graph
neural network based approach for tackling the problem of interactive video
object segmentation. Our network operates on superpixel-graphs which allow us
to reduce the dimensionality of the problem by several magnitudes. We show,
that our network possessing only a few thousand parameters is able to achieve
state-of-the-art performance, while inference remains fast and can be trained
quickly with very little data.
