  The idea behind object-centric representation learning is that natural scenes
can better be modeled as compositions of objects and their relations as opposed
to distributed representations. This inductive bias can be injected into neural
networks to potentially improve systematic generalization and performance of
downstream tasks in scenes with multiple objects. In this paper, we train
state-of-the-art unsupervised models on five common multi-object datasets and
evaluate segmentation metrics and downstream object property prediction. In
addition, we study generalization and robustness by investigating the settings
where either a single object is out of distribution -- e.g., having an unseen
color, texture, or shape -- or global properties of the scene are altered --
e.g., by occlusions, cropping, or increasing the number of objects. From our
experimental study, we find object-centric representations to be useful for
downstream tasks and generally robust to most distribution shifts affecting
objects. However, when the distribution shift affects the input in a less
structured manner, robustness in terms of segmentation and downstream task
performance may vary significantly across models and distribution shifts.
