[2006.15055] Object-Centric Learning with Slot Attentionopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

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.

4 mentions: @thomaskipf
Keywords: attention
Date: 2020/06/29 12:58

Referring Tweets

@thomaskipf Excited to share our work @GoogleAI on Object-centric Learning with Slot Attention! Slot Attention is a simple module for structure discovery and set prediction: it uses iterative attention to group perceptual inputs into a set of slots. Paper: t.co/xfpjQwMuLP [1/7] t.co/0CzWO9B1fV

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