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Visual Program Distillation with Template-Based Augmentation

large-language-models › model-architecture

📄 Abstract

Abstract: Adapting visual programming or prompting large language models (LLMs) to generate executable code for visual tasks like visual question answering (VQA) for specialized tasks or domains remains challenging due to high annotation and inference costs. We propose a low-cost visual program distillation method that can be used for models with at most 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 specialized visual programs with the added benefit of much faster inference

Key Contributions

Proposes a low-cost visual program distillation method using template-based augmentation to generate specialized visual programs for tasks like VQA. This method requires no human-generated program annotations and enables smaller language models (<=1B parameters) to generate high-quality programs with significantly faster inference.

Business Value

Reduces the cost and time required to develop specialized AI models for visual tasks, making advanced capabilities like VQA more accessible and efficient for various applications.

Paper Metadata

Innovation Type

Distillation and Data Augmentation Technique

Deployment Feasibility

Highly feasible, as it focuses on enabling smaller, faster models, which are easier to deploy.

Limitations Addressed

High annotation and inference costs associated with adapting LLMs for visual programming tasks, and the need for human-generated program annotations.

Performance Gains

Enables smaller language models to generate high-quality specialized visual programs with much faster inference.

Technical Tags

Visual Program DistillationTemplate-Based AugmentationLarge Language Models (LLMs)Visual Question Answering (VQA)Code GenerationLow-Cost AnnotationParameter EfficiencyInference SpeedSpecialized ModelsSynthetic Data

Research Topics

Visual ReasoningProgram SynthesisLLM AdaptationEfficient AIData Augmentation

Methods & Architectures

Program DistillationTemplate-Based AugmentationSynthetic Data GenerationLow-Annotation Learning Small Language Models (up to 1B parameters)

Applications & Tasks

Computer Vision Natural Language Processing Human-Computer Interaction Model AdaptationData ScarcityAnnotation Cost ReductionEfficiency Improvement Generating Visual ProgramsVisual Question AnsweringAdapting LLMs for Visual Tasks

Related Fields

Computer VisionNatural Language ProcessingMachine LearningProgram SynthesisEfficient Deep Learning

Keywords

Visual ProgrammingLLMsDistillationData AugmentationTemplateVQACode GenerationEfficient AILow-CostSynthetic DataParameter EfficiencyInference Speed

Academic Context

#Visual Reasoning#Program Synthesis#LLM Adaptation#Efficient AI#Data Augmentation

Commercial Potential

Potential Products

Specialized VQA ModelsTools for Generating Visual ProgramsEfficient LLM Adaptation Frameworks

Target Industries

TechnologyMediaE-commerceEducationComputer Vision Applications

Use Case Examples

Creating efficient VQA models for specific image domains (e.g., medical, retail)Enabling faster generation of executable code for visual tasksReducing the computational cost of deploying visual AI models

Competitive Edge

Offers a novel, low-cost approach to adapt LLMs for visual tasks by distilling knowledge into smaller models using synthetic data, overcoming annotation bottlenecks and improving inference efficiency.

Market Opportunity

Large and growing market for efficient AI models and visual reasoning applications.

Revenue Models

Licensing of the techniquedevelopment of specialized modelsconsulting.

Resource Requirements

Compute Needs

Moderate for training smaller models; potentially high for generating synthetic data.

Data Requirements

Requires question/answer data, but not human-generated program annotations. Template definitions are crucial.

Deployment Constraints

Effectiveness depends on the quality of templates and synthetic data,Generalization to unseen visual tasks might be limited

Scalability

Scalable due to the focus on smaller models and synthetic data generation.

Regulatory Considerations

N/A directly.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years for productization.

Licensing

Likely research-only or requires specific licensing.

Patent Potential

Moderate, for the template-based augmentation and distillation technique.

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