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.

本文研究了利用自监督学习获取对象集中表示的关键方面，并在 CLEVR 数据集上运行了一些实验来验证我们的见解。我们的结果表明，竞争式注意力对象发现对于学习对象集中表示非常重要。对于训练，我们证明了在潜在空间中应用具有匹配性的对比损失，避免了基于像素的重建。但是，该优化目标对假阴性（重复对象）和假阳性（匹配错误）非常敏感。因此，需要仔细考虑数据增强和负样本选择。