Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high computational cost; there are no object-level relational factors within slots. Synchrony-based models in principle can address these limitations by using complex-valued activations which store binding information in their phase components. However, working examples of such synchrony-based models have been developed only very recently, and are still limited to toy grayscale datasets and simultaneous storage of less than three objects in practice. Here we introduce architectural modifications and a novel contrastive learning method that greatly improve the state-of-the-art synchrony-based model. For the first time, we obtain a class of synchrony-based models capable of discovering objects in an unsupervised manner in multi-object color datasets and simultaneously representing more than three objects

翻译：当前最先进的对象中心模型使用槽位和基于注意力的路由机制进行绑定。然而，该类模型存在若干概念性限制：槽位数量固定不变；所有槽位容量相等；训练计算成本高昂；槽位内部缺乏对象层面的关系因子。基于同步的模型原则上可通过使用复值激活（将绑定信息存储于其相位分量中）来解决这些限制。然而，此类基于同步模型的工作实例直至近期才被开发出来，且在实际应用中仍局限于玩具级灰度数据集及同时存储少于三个对象的场景。本文引入架构上的改进与一种新颖的对比学习方法，显著提升了当前最先进的基于同步模型的表现。我们首次获得了一类基于同步的模型，该模型能够在多对象彩色数据集中以无监督方式发现对象，并同时表示三个以上的对象。