Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks
Baoxia Du, Hongyang Du, Dusit Niyato, Ruidong Li·May 05, 2025
Summary
LLaVA, a large language and vision assistant, optimizes energy use and accuracy in vehicle networks. It uses multimodal models to address challenges like scalability, data security, and costs. A lightweight object detection model, trained on 166 images, excels in real-time processing. Vicuna-7B, FA-SemCom, and attention mechanisms enhance model accuracy, especially in low SNR scenarios. The framework boosts model robustness across domains, focusing on ship detection, visual attention, and mathematical reasoning. Recent advancements include gaze prediction, faster inference, and large language-and-vision assistants.
Introduction
Background
Overview of large language and vision assistants (LLaVA)
Importance of optimizing energy use and accuracy in vehicle networks
Objective
The goal of LLaVA in addressing challenges like scalability, data security, and costs
Method
Multimodal Models
Utilization of multimodal models for comprehensive data processing
Enhancing model performance through integration of language and vision
Lightweight Object Detection Model
Description of the lightweight object detection model
Training details and its real-time processing capabilities
Vicuna-7B, FA-SemCom, and Attention Mechanisms
Explanation of Vicuna-7B and its role in model enhancement
Overview of FA-SemCom and its impact on model accuracy
Importance of attention mechanisms in improving model performance, especially in low SNR scenarios
Domain-Specific Enhancements
Focus on ship detection, visual attention, and mathematical reasoning
Description of how the framework boosts model robustness across different domains
Recent Advancements
Gaze Prediction
Explanation of gaze prediction capabilities in LLaVA
Significance in enhancing user interaction and experience
Faster Inference
Overview of techniques for accelerating model processing
Benefits in real-time applications and efficiency
Large Language-and-Vision Assistants
Discussion on the development and application of large-scale LLaVA systems
Future directions and potential impact on vehicle networks and beyond
Basic info
papers
artificial intelligence
Advanced features