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插槽和基于注意力的路由机制进行绑定。然而，这类模型存在若干概念性局限：插槽数量被硬编码；所有插槽容量相等；训练计算成本高；插槽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数激活值（其相位分量存储绑定信息）来解决这些局限。然而，此类基于同步的模型工作实例直到最近才被开发出来，且实际应用中仍局限于灰度玩具数据集，同时存储的对象数不超过三个。本文引入架构改进及一种新颖的对比学习方法，大幅提升了现有最先进同步模型的性能。我们首次获得了一类可在多对象彩色数据集中以无监督方式发现对象、并同时表示超过三个对象的同步模型。