Object-Centric Learning with Slot Mixture Module

Daniil Kirilenko, Vitaliy Vorobyov, Alexey Kovalev, Aleksandr Panov

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Keywords: Object-centric representations, Gaussian Mixture Model, Slot Attention, Set Prediction Task

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TL;DR: We proposed a generalization of a slot-based approach for object-centric representations as a Slot Mixture Model that allows state-of-the-art performance in the set property prediction and object discovery tasks.

Abstract: Object-centric architectures usually apply a differentiable module to the entire feature map to decompose it into sets of entity representations called slots. Some of these methods structurally resemble clustering algorithms, where the cluster's center in latent space serves as a slot representation. Slot Attention is an example of such a method, acting as a learnable analog of the soft k-means algorithm. Our work employs a learnable clustering method based on the Gaussian Mixture Model. Unlike other approaches, we represent slots not only as centers of clusters but also incorporate information about the distance between clusters and assigned vectors, leading to more expressive slot representations. Our experiments demonstrate that using this approach instead of Slot Attention improves performance in object-centric scenarios, achieving state-of-the-art results in the set property prediction task.

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Primary Area: representation learning for computer vision, audio, language, and other modalities

Submission Number: 5838