论文标题
以对象为中心的学习和插槽的关注
Object-Centric Learning with Slot Attention
论文作者
论文摘要
学习复杂场景的以对象为中心的表示是从低级感知特征中实现有效的抽象推理的有希望的一步。但是，大多数深度学习方法都学习分布式表示，这些表示不会捕获自然场景的组成特性。在本文中，我们介绍了插槽注意模块，该模块是一种与感知表示的架构组件，例如卷积神经网络的输出，并产生一组我们称为插槽的任务依赖的抽象表示。这些插槽是可以交换的，可以通过在多轮注意力的情况下通过竞争性程序进行专业的过程来与输入中的任何对象结合。我们从经验上证明，插槽注意力可以提取以对象为中心的表示，从而使概括在接受无监督的对象发现和监督的属性预测任务进行培训时，可以概括地看不见的构图。
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.
