  The ability to decompose scenes into their object components is a desired
property for autonomous agents, allowing them to reason and act in their
surroundings. Recently, different methods have been proposed to learn
object-centric representations from data in an unsupervised manner. These
methods often rely on latent representations learned by deep neural networks,
hence requiring high computational costs and large amounts of curated data.
Such models are also difficult to interpret. To address these challenges, we
propose the Phase-Correlation Decomposition Network (PCDNet), a novel model
that decomposes a scene into its object components, which are represented as
transformed versions of a set of learned object prototypes. The core building
block in PCDNet is the Phase-Correlation Cell (PC Cell), which exploits the
frequency-domain representation of the images in order to estimate the
transformation between an object prototype and its transformed version in the
image. In our experiments, we show how PCDNet outperforms state-of-the-art
methods for unsupervised object discovery and segmentation on simple benchmark
datasets and on more challenging data, while using a small number of learnable
parameters and being fully interpretable.
