  Humans perceive the seemingly chaotic world in a structured and compositional
way with the prerequisite of being able to segregate conceptual entities from
the complex visual scenes. The mechanism of grouping basic visual elements of
scenes into conceptual entities is termed as perceptual grouping. In this work,
we propose a new type of spatial mixture models with learnable priors for
perceptual grouping. Different from existing methods, the proposed method
disentangles the attributes of an object into ``shape'' and ``appearance''
which are modeled separately by the mixture weights and the mixture components.
More specifically, each object in the visual scene is fully characterized by
one latent representation, which is in turn transformed into parameters of the
mixture weight and the mixture component by two neural networks. The mixture
weights focus on modeling spatial dependencies (i.e., shape) and the mixture
components deal with intra-object variations (i.e., appearance). In addition,
the background is separately modeled as a special component complementary to
the foreground objects. Our extensive empirical tests on two perceptual
grouping datasets demonstrate that the proposed method outperforms the
state-of-the-art methods under most experimental configurations. The learned
conceptual entities are generalizable to novel visual scenes and insensitive to
the diversity of objects. Code is available at
https://github.com/jinyangyuan/learnable-deep-priors.
