arxiv_cv 95% Match Research Paper AI Researchers,ML Engineers,Medical Imaging Specialists,Domain Experts in specialized fields 1 month ago

LEAML: Label-Efficient Adaptation to Out-of-Distribution Visual Tasks for Multimodal Large Language Models

large-language-models › multimodal-llms

📄 Abstract

Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such as medical imaging, where labeled data is limited and expensive. We introduce LEAML, a label-efficient adaptation framework that leverages both scarce labeled VQA samples and abundant unlabeled images. Our approach generates domain-relevant pseudo question-answer pairs for unlabeled data using a QA generator regularized by caption distillation. Importantly, we selectively update only those neurons most relevant to question-answering, enabling the QA Generator to efficiently acquire domain-specific knowledge during distillation. Experiments on gastrointestinal endoscopy and sports VQA demonstrate that LEAML consistently outperforms standard fine-tuning under minimal supervision, highlighting the effectiveness of our proposed LEAML framework.

Key Contributions

Introduces LEAML, a label-efficient adaptation framework for MLLMs facing out-of-distribution tasks with limited labeled data. It leverages scarce labeled VQA samples and abundant unlabeled images by generating pseudo-QA pairs via a QA generator regularized by caption distillation, selectively updating relevant neurons for efficient domain knowledge acquisition.

Business Value

Enables the application of powerful MLLMs to specialized domains with scarce data, such as medical diagnostics or niche industrial applications, reducing the need for extensive data labeling efforts.

Paper Metadata

Innovation Type

Framework/Methodology

Deployment Feasibility

Moderate. Requires access to unlabeled domain-specific images and potentially some labeled VQA data. The adaptation process itself might require significant computation.

Limitations Addressed

MLLMs struggle with out-of-distribution tasks in specialized domains (like medical imaging) due to limited and expensive labeled data. Addresses the inefficiency of standard fine-tuning under minimal supervision.

Performance Gains

Consistently outperforms standard fine-tuning under minimal supervision.

Technical Tags

label-efficient learningout-of-distribution adaptationmultimodal large language models (MLLMs)visual question answering (VQA)pseudo-labelingcaption distillationneuron selectiondomain adaptation

Research Topics

Few-Shot LearningDomain AdaptationMultimodal AIMedical ImagingVisual Question Answering

Methods & Architectures

LEAML frameworkQA GeneratorCaption DistillationSelective Neuron UpdatePseudo-label Generation Multimodal Large Language Models (MLLMs)

Applications & Tasks

Medical Imaging Gastrointestinal Endoscopy Sports Analytics Specialized VQA Out-of-Distribution (OOD) AdaptationLabel-Efficient LearningDomain-Specific VQAHandling Limited Labeled Data Adapting MLLMs to OOD Visual TasksFew-shot VQAMedical Image Analysis

Datasets & Benchmarks

Benchmarks

Gastrointestinal endoscopy VQA • Sports VQA

VQA Accuracy

Related Fields

Large Language ModelsMultimodal AIComputer VisionMedical InformaticsFew-Shot LearningDomain Adaptation

Keywords

Multimodal LLMMLLMLabel-EfficientOut-of-DistributionDomain AdaptationVQAMedical ImagingFew-Shot LearningPseudo-LabelingCaption DistillationGastrointestinal EndoscopyComputer Vision

Academic Context

#Few-Shot Learning#Domain Adaptation#Multimodal AI#Medical Imaging#Visual Question Answering

Commercial Potential

Potential Products

AI diagnostic assistants for medical imagingSpecialized VQA systems for niche industriesTools for adapting foundation models to specific domains

Target Industries

HealthcareMedical DevicesSports AnalyticsManufacturingResearch

Use Case Examples

Answering questions about endoscopic images for diagnosisAnalyzing sports actions based on visual data and commentaryAdapting general AI models for specific scientific research tasks

Competitive Edge

Provides a more efficient and effective way to adapt large multimodal models to specialized domains compared to standard fine-tuning, especially when labeled data is scarce.

Market Opportunity

Large and growing market for AI in healthcare and specialized domain applications.

Revenue Models

Licensing of the LEAML frameworkdevelopment of domain-specific AI solutions.

Resource Requirements

Compute Needs

High (for training/adaptation of MLLMs)

Data Requirements

Abundant unlabeled domain-specific images, scarce labeled VQA samples.

Deployment Constraints

Need for domain-specific unlabeled data, computational resources for adaptation.

Scalability

Scalability depends on the underlying MLLM and the efficiency of the adaptation process.

Regulatory Considerations

Data privacy (especially for medical data)AI in healthcare regulations

Production Readiness

Maturity Level

Research

Time to Market

2-4 years

Patent Potential

Moderate (novel adaptation framework)

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