  Self-supervision allows learning meaningful representations of natural
images, which usually contain one central object. How well does it transfer to
multi-entity scenes? We discuss key aspects of learning structured
object-centric representations with self-supervision and validate our insights
through several experiments on the CLEVR dataset. Regarding the architecture,
we confirm the importance of competition for attention-based object discovery,
where each image patch is exclusively attended by one object. For training, we
show that contrastive losses equipped with matching can be applied directly in
a latent space, avoiding pixel-based reconstruction. However, such an
optimization objective is sensitive to false negatives (recurring objects) and
false positives (matching errors). Careful consideration is thus required
around data augmentation and negative sample selection.
