arxiv_cv 90% Match Research Paper AI Researchers,Machine Learning Engineers,Developers of multimodal systems 2 weeks ago

Scope: Selective Cross-modal Orchestration of Visual Perception Experts

large-language-models › multimodal-llms

📄 Abstract

Abstract: Vision-language models (VLMs) benefit from multiple vision encoders, but naively stacking them yields diminishing returns while multiplying inference costs. We propose SCOPE, a Mixture-of-Encoders (MoEnc) framework that dynamically selects one specialized encoder per image-text pair via instance-level routing, unlike token-level routing in traditional MoE. SCOPE maintains a shared encoder and a pool of routed encoders. A lightweight router uses cross-attention between text prompts and shared visual features to select the optimal encoder from the routed encoders. To train this router, we introduce dual entropy regularization with auxiliary losses to balance dataset-level load distribution with instance-level routing confidence. Remarkably, SCOPE with one shared plus one routed encoder outperforms models using all four extra encoders simultaneously, while reducing compute by 24-49\%. This demonstrates that intelligent encoder selection beats brute-force aggregation, challenging the prevailing paradigm in multi-encoder VLMs.

Authors (8)

Tianyu Zhang

Suyuchen Wang

Chao Wang

Juan Rodriguez

Ahmed Masry

Xiangru Jian

+2 more

Submitted

October 14, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

SCOPE proposes a Mixture-of-Encoders (MoEnc) framework with instance-level routing for VLMs, dynamically selecting specialized vision encoders per input pair. This approach significantly reduces inference costs (24-49%) while outperforming models using all encoders simultaneously, demonstrating the effectiveness of intelligent selection over brute-force aggregation.

Business Value

Enables the development of more powerful and efficient VLMs, making advanced multimodal AI applications more accessible and cost-effective for businesses.

Paper Metadata

Innovation Type

Architectural Innovation

Deployment Feasibility

High. The framework is designed for efficiency and can be integrated into existing VLM architectures. Instance-level routing is more adaptable than token-level.

Limitations Addressed

Diminishing returns from naively stacking multiple vision encoders,Multiplying inference costs in VLMs,Inefficiency of token-level routing in Mixture-of-Experts (MoE) for vision encoders

Performance Gains

Reduced compute cost (24-49%),Improved performance compared to using all encoders

Technical Tags

vision-language models (VLMs)mixture-of-encoders (MoEnc)instance-level routingcross-modal attentionencoder selectioncomputational efficiencydeep learningnatural language processingcomputer vision

Research Topics

Multimodal LearningVision-Language ModelsModel EfficiencyDeep Learning ArchitecturesNatural Language Processing

Methods & Architectures

Mixture-of-Encoders (MoEnc)Instance-level RoutingCross-Attention RouterDual Entropy RegularizationAuxiliary Losses Shared EncoderRouted EncodersLightweight Router Network

Applications & Tasks

Image Captioning Visual Question Answering Robotics Autonomous Driving Content Moderation Multimodal UnderstandingComputational Cost ReductionModel Specialization Image-Text MatchingVisual Question AnsweringImage Captioning

Related Fields

Artificial IntelligenceMachine LearningNatural Language ProcessingComputer VisionDeep Learning Architectures

Keywords

Vision-language modelsMultimodal AIMixture-of-ExpertsEncoder selectionComputational efficiencyInstance-level routingCross-modal learningDeep learningNLPComputer vision

Academic Context

#Multimodal Learning#Vision-Language Models#Model Efficiency#Deep Learning Architectures#Natural Language Processing

Commercial Potential

Potential Products

More efficient multimodal search enginesSmarter AI assistantsAdvanced image/video analysis tools

Target Industries

TechnologyE-commerceMediaSecurity

Use Case Examples

Building a VLM that can answer complex questions about images more quicklyDeveloping a system for automated image tagging and description generation with lower latency

Competitive Edge

Offers a more efficient alternative to dense multimodal models by intelligently selecting expert encoders, potentially leading to better performance-per-compute ratio.

Market Opportunity

Growing market for multimodal AI solutions.

Revenue Models

Licensing of the SCOPE frameworkintegration into cloud AI services.

Resource Requirements

Compute Needs

Reduced inference compute compared to dense models, but training may still be intensive.

Data Requirements

Requires large-scale multimodal datasets for training the router and encoders.

Deployment Constraints

Requires careful selection and training of specialized encoders.

Scalability

Scalable due to efficient routing mechanism, allowing for more expert encoders to be added.

Regulatory Considerations

Standard AI ethics and bias considerations for multimodal systems.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into commercial products.

Patent Potential

Moderate, due to novel routing and MoEnc architecture for VLMs.

View Full Paper Back to Papers