In this paper, we propose a novel object-centric representation, called Block-Slot Representation. Unlike the conventional slot representation, the Block-Slot Representation provides concept-level disentanglement within a slot. A block-slot is constructed by composing a set of modular concept representations, called blocks, generated from a learned memory of abstract concept prototypes. We call this block-slot construction process Block-Slot Attention. Block-Slot Attention facilitates the emergence of abstract concept blocks within a slot such as color, position, and texture, without any supervision. This brings the benefits of disentanglement into slots and the representation becomes more interpretable. Similar to Slot Attention, this mechanism can be used as a drop-in module in any arbitrary neural architecture. In experiments, we show that our model disentangles object properties significantly better than the previous methods, including complex textured scenes. We also demonstrate the ability to compose novel scenes by composing slots at the block-level.

本文提出了一种名为Neural Systematic Binder或SysBinder的神经机制，用于构建一种新的结构化表示，即块槽表示，用于促进系统性推广。SysBinder以无监督的方式获得这种结构，其可以应用于任何模态，并可以作为任意神经网络的调入模块。实验表明，SysBinder在CLEVR-Tex等视觉复杂场景图像中提供了更好的因素解缠，同时在受控场景生成中演示了因素级系统性。

神经系统结合器