  Humans naturally decompose their environment into entities at the appropriate
level of abstraction to act in the world. Allowing machine learning algorithms
to derive this decomposition in an unsupervised way has become an important
line of research. However, current methods are restricted to simulated data or
require additional information in the form of motion or depth in order to
successfully discover objects. In this work, we overcome this limitation by
showing that reconstructing features from models trained in a self-supervised
manner is a sufficient training signal for object-centric representations to
arise in a fully unsupervised way. Our approach, DINOSAUR, significantly
out-performs existing image-based object-centric learning models on simulated
data and is the first unsupervised object-centric model that scales to
real-world datasets such as COCO and PASCAL VOC. DINOSAUR is conceptually
simple and shows competitive performance compared to more involved pipelines
from the computer vision literature.
