Learning to Infer Generative Template Programs for Visual Concepts
This addresses the challenge of few-shot visual concept learning for AI systems, offering a domain-general approach that is incremental compared to existing domain-specific methods.
The paper tackles the problem of learning flexible visual concepts from few examples by introducing a neurosymbolic system that infers generative template programs, which capture structural and parametric patterns in a domain-general way. It shows that the method outperforms task-specific alternatives and is competitive with domain-specific approaches in experiments across 2D layouts, Omniglot characters, and 3D shapes.
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.