Object-Centric Representations Generalize Better Compositionally with Less Compute

Abstract

Compositional generalization—the ability to reason about novel combinations of familiar concepts—is fundamental to human cognition and a critical challenge for machine learning. Object-Centric representation learning has been proposed as a promising approach for achieving this capability. However, systematic evaluation of these methods in visually complex settings remains limited. In this work, we introduce a benchmark to measure how well vision encoders, with and without object-centric biases, generalize to unseen combinations of object properties. Using CLEVRTex-style images, we create multiple training splits with partial coverage of object property combinations and generate question--answer pairs to assess compositional generalization on a held-out test set. We focus on comparing pretrained foundation models with object-centric models that incorporate such foundation models as backbones---a leading approach in this domain. To ensure a fair and comprehensive comparison, we carefully account for representation format differences. In this preliminary study, we use DINOv2 as the foundation model and DINOSAURv2 as its object-centric counterpart. We control for compute budget and differences in image representation sizes to ensure robustness. Our key findings reveal that object-centric approaches (1) converge faster on in-distribution data but underperform slightly when non-object-centric models are given a significant compute advantage, and (2) they exhibit superior compositional generalization, outperforming DINOv2 on unseen combinations of object properties while requiring approximately four to eight times less downstream compute.