  The aim of object-centric vision is to construct an explicit representation
of the objects in a scene. This representation is obtained via a set of
interchangeable modules called \emph{slots} or \emph{object files} that compete
for local patches of an image. The competition has a weak inductive bias to
preserve spatial continuity; consequently, one slot may claim patches scattered
diffusely throughout the image. In contrast, the inductive bias of human vision
is strong, to the degree that attention has classically been described with a
spotlight metaphor. We incorporate a spatial-locality prior into
state-of-the-art object-centric vision models and obtain significant
improvements in segmenting objects in both synthetic and real-world datasets.
Similar to human visual attention, the combination of image content and spatial
constraints yield robust unsupervised object-centric learning, including less
sensitivity to model hyperparameters.
