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采用槽和基于注意力的路由机制实现绑定，但此类模型存在若干概念性缺陷：槽数量硬编码、所有槽容量均等、训练计算成本高、槽内缺乏对象级关系因子。基于同步的模型原则上可通过使用复数激活函数，在相位分量中存储绑定信息来克服这些局限。然而，此类同步模型的工作实例直到最近才被开发，且实际应用中仍局限于玩具级灰度数据集和同时存储少于三个对象。本文引入架构改进与新型对比学习方法，显著提升了当前最先进的同步模型性能。我们首次获得一类同步模型，能够在多对象彩色数据集中以无监督方式发现目标，并同时表征超过三个对象。