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Title: Learning to infer generative template programs for visual concepts
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.  more » « less
Award ID(s):
1941808
PAR ID:
10580883
Author(s) / Creator(s):
; ;
Publisher / Repository:
ICML'24: Proceedings of the 41st International Conference on Machine Learning
Date Published:
Format(s):
Medium: X
Location:
Vienna, Austria
Sponsoring Org:
National Science Foundation
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