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Humans perceive the seemingly chaotic world in a structured and compositionalway with the prerequisite of being able to segregate conceptual entities fromthe complex visual scenes. The mechanism of grouping basic visual elements ofscenes into conceptual entities is termed as perceptual grouping. In this work,we propose a new type of spatial mixture models with learnable priors forperceptual grouping. Different from existing methods, the proposed methoddisentangles 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 byone latent representation, which is in turn transformed into parameters of themixture weight and the mixture component by two neural networks. The mixtureweights focus on modeling spatial dependencies (i.e., shape) and the mixturecomponents deal with intra-object variations (i.e., appearance). In addition,the background is separately modeled as a special component complementary tothe foreground objects. Our extensive empirical tests on two perceptualgrouping datasets demonstrate that the proposed method outperforms thestate-of-the-art methods under most experimental configurations. The learnedconceptual entities are generalizable to novel visual scenes and insensitive tothe diversity of objects. Code is available athttps://github.com/jinyangyuan/learnable-deep-priors.

Spatial Mixture Models with Learnable Deep Priors for Perceptual Grouping