  The key to high-level cognition is believed to be the ability to
systematically manipulate and compose knowledge pieces. While token-like
structured knowledge representations are naturally provided in text, it is
elusive how to obtain them for unstructured modalities such as scene images. In
this paper, we propose a neural mechanism called Neural Systematic Binder or
SysBinder for constructing a novel structured representation called Block-Slot
Representation. In Block-Slot Representation, object-centric representations
known as slots are constructed by composing a set of independent factor
representations called blocks, to facilitate systematic generalization.
SysBinder obtains this structure in an unsupervised way by alternatingly
applying two different binding principles: spatial binding for spatial
modularity across the full scene and factor binding for factor modularity
within an object. SysBinder is a simple, deterministic, and general-purpose
layer that can be applied as a drop-in module in any arbitrary neural network
and on any modality. In experiments, we find that SysBinder provides
significantly better factor disentanglement within the slots than the
conventional object-centric methods, including, for the first time, in visually
complex scene images such as CLEVR-Tex. Furthermore, we demonstrate
factor-level systematicity in controlled scene generation by decoding unseen
factor combinations.
