Learning to Infer Generative Template Programs for Visual Concepts
R. Kenny Jones Siddhartha Chaudhuri Daniel Ritchie
People grasp flexible visual concepts from a few examples. We explore a
neurosymbolic system that learns how to infer programs that capture visual
concepts in a domain-general fashion. We introduce Template Programs:
programmatic expressions from a domain-specific language that specify
structural and parametric patterns common to an input concept. Our framework
supports multiple concept-related tasks, including few-shot generation and
co-segmentation through parsing. We develop a learning paradigm that allows us
to train networks that infer Template Programs directly from visual datasets
that contain concept groupings. We run experiments across multiple visual
domains: 2D layouts, Omniglot characters, and 3D shapes. We find that our
method outperforms task-specific alternatives, and performs competitively
against domain-specific approaches for the limited domains where they exist.