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.

本文提出了一种在现实和合成数据集中具有显著改进的物体分割方法，称为对象中心视觉，它在现有模型中引入了空间局部性先验，并模拟了人类视觉注意力的功能。