  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.
