  Existing deep learning based unsupervised video object segmentation methods
still rely on ground-truth segmentation masks to train. Unsupervised in this
context only means that no annotated frames are used during inference. As
obtaining ground-truth segmentation masks for real image scenes is a laborious
task, we envision a simple framework for dominant moving object segmentation
that neither requires annotated data to train nor relies on saliency priors or
pre-trained optical flow maps. Inspired by a layered image representation, we
introduce a technique to group pixel regions according to their affine
parametric motion. This enables our network to learn segmentation of the
dominant foreground object using only RGB image pairs as input for both
training and inference. We establish a baseline for this novel task using a new
MovingCars dataset and show competitive performance against recent methods that
require annotated masks to train.
