Template-Based Visual Program Distillation

For users with limited computational resources, visual programming or prompting large language models (LLMs) to generate executable code for visual tasks, like visual question answering (VQA), remains largely inaccessible. Even with techniques such as distillation, adapting visual programming to smaller models or specific datasets is still quite challenging due to high annotation costs. We propose a low-cost visual program distillation method that can be used for models with fewer than 1 billion parameters and requires no human-generated program annotations. We achieve this through synthetic data augmentation based on decoupling programs into higher-level skills, called templates, and their corresponding arguments. Experimental results show that, with a relatively small amount of question/answer data, small language models can generate high-quality visual programs with the added benefit of much faster inference.