Learning object-centric representations of complex scenes is a promising step
towards enabling efficient abstract reasoning from low-level perceptual
features. Yet, most deep learning approaches learn distributed representations
that do not capture the compositional properties of natural scenes. In this
paper, we present the Slot Attention module, an architectural component that
interfaces with perceptual representations such as the output of a
convolutional neural network and produces a set of task-dependent abstract
representations which we call slots. These slots are exchangeable and can bind
to any object in the input by specializing through a competitive procedure over
multiple rounds of attention. We empirically demonstrate that Slot Attention
can extract object-centric representations that enable generalization to unseen
compositions when trained on unsupervised object discovery and supervised
property prediction tasks.

本文提出了一个名为 Slot Attention 的架构组件，它能够从低级感知特征中提取物体为中心的表示，并能够推广到未见组合。