Contrastive, self-supervised learning of object representations recently
emerged as an attractive alternative to reconstruction-based training. Prior
approaches focus on contrasting individual object representations (slots)
against one another. However, a fundamental problem with this approach is that
the overall contrastive loss is the same for (i) representing a different
object in each slot, as it is for (ii) (re-)representing the same object in all
slots. Thus, this objective does not inherently push towards the emergence of
object-centric representations in the slots. We address this problem by
introducing a global, set-based contrastive loss: instead of contrasting
individual slot representations against one another, we aggregate the
representations and contrast the joined sets against one another. Additionally,
we introduce attention-based encoders to this contrastive setup which
simplifies training and provides interpretable object masks. Our results on two
synthetic video datasets suggest that this approach compares favorably against
previous contrastive methods in terms of reconstruction, future prediction and
object separation performance.

通过引入全局、基于集合的对比损失和基于注意力的编码器，将个体目标表示相互对比更改为聚合表示并相互对比，从而在自监督学习中更有效地推动物体中心表示的出现，并在两个合成视频数据集上得到更好的效果。