  Recently developed deep learning models are able to learn to segment scenes
into component objects without supervision. This opens many new and exciting
avenues of research, allowing agents to take objects (or entities) as inputs,
rather that pixels. Unfortunately, while these models provide excellent
segmentation of a single frame, they do not keep track of how objects segmented
at one time-step correspond (or align) to those at a later time-step. The
alignment (or correspondence) problem has impeded progress towards using object
representations in downstream tasks. In this paper we take steps towards
solving the alignment problem, presenting the AlignNet, an unsupervised
alignment module.
