  Learning object-centric representations from complex natural environments
enables both humans and machines with reasoning abilities from low-level
perceptual features. To capture compositional entities of the scene, we
proposed cyclic walks between perceptual features extracted from vision
transformers and object entities. First, a slot-attention module interfaces
with these perceptual features and produces a finite set of slot
representations. These slots can bind to any object entities in the scene via
inter-slot competitions for attention. Next, we establish entity-feature
correspondence with cyclic walks along high transition probability based on the
pairwise similarity between perceptual features (aka "parts") and slot-binded
object representations (aka "whole"). The whole is greater than its parts and
the parts constitute the whole. The part-whole interactions form cycle
consistencies, as supervisory signals, to train the slot-attention module. Our
rigorous experiments on \textit{seven} image datasets in \textit{three}
\textit{unsupervised} tasks demonstrate that the networks trained with our
cyclic walks can disentangle foregrounds and backgrounds, discover objects, and
segment semantic objects in complex scenes. In contrast to object-centric
models attached with a decoder for the pixel-level or feature-level
reconstructions, our cyclic walks provide strong learning signals, avoiding
computation overheads and enhancing memory efficiency. Our source code and data
are available at:
\href{https://github.com/ZhangLab-DeepNeuroCogLab/Parts-Whole-Object-Centric-Learning/}{link}.
