Object-Centric Learning with Slot Mixture Module

2023-11-08

International Conference on Learning Representations

AI Summary

This work addresses unsupervised object decomposition to enhance object-centric scene understanding, particularly for set-wise attribute prediction. We propose a clustering slot module based on a learnable Gaussian Mixture Model (GMM), wherein each slot is modeled as a cluster centroid jointly embedded with distance-aware representations relative to other clusters—replacing conventional Slot Attention. To our knowledge, this is the first incorporation of GMM into slot-based learning, explicitly encoding inter-cluster structural relationships to improve representation discriminability and geometric consistency. Our method integrates soft assignment, end-to-end optimization, and slot-wise distance-aware embedding. Evaluated on CLEVR6 and Multi-dSprites, it significantly outperforms Slot Attention and other baselines: set-wise attribute prediction accuracy improves by 3.2–5.7 percentage points, achieving state-of-the-art performance.

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.

Problem

• Object-Centric Scene Understanding
• Attribute Prediction
• Performance Improvement

Innovation

• Gaussian Mixture Model
• Learnable Classification Method
• Slot Enrichment