何时多模态AI有所帮助？视觉-语言模型与CNN在卫星-地面网络中的频谱管理中的诊断互补性
When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks
AI总结：
本文研究了视觉-语言模型与CNN在频谱管理中的互补性，提出SpectrumQA基准测试，并发现CNN在空间定位任务中表现优异，而VLM在语义推理任务中具有独特优势，通过任务类型路由器可提升整体性能。
摘要：
本文探讨了视觉-语言模型（VLMs）在无线网络管理中的应用加速，但缺乏对这些大基础模型在频谱相关任务中超越轻量级卷积神经网络（CNNs）的系统理解。本文首次对VLMs和CNNs在非 terrestrial网络和 terrestrial网络（NTN-TN）协作系统中的频谱热图理解进行了诊断比较。我们引入了SpectrumQA基准测试，包含108K个视觉问题-答案对，涵盖四个粒度级别：场景分类（L1）、区域推理（L2）、空间定位（L3）和语义推理（L4）。在三个NTN-TN场景中，使用冻结的Qwen2-VL-7B和训练的ResNet-18进行实验，揭示了任务依赖的互补性：CNN在严重性分类（L1）中达到72.9%的准确率，在空间定位（L3）中达到0.552的IoU，而VLM通过仅使用三个上下文示例实现了语义推理（L4）的F1=0.576，这一能力在CNN架构中根本不存在。链式思维（CoT）提示进一步提高了VLM的推理能力，提升了12.6%（F1: 0.209->0.233），但对空间任务无影响，证实互补性源于架构差异而非提示限制。一个确定性的任务类型路由器将监督任务委托给CNN，将推理任务委托给VLM，实现了综合得分为0.616，比CNN单独使用提高了39.1%。我们进一步表明，VLM表示在跨场景鲁棒性方面更强，在6个转移方向中的5个方向中性能下降较小。这些发现提供了可操作的指导：部署CNN用于空间定位和VLM用于语义频谱推理，而不是将它们视为替代品。
英文摘要：
The adoption of vision-language models (VLMs) for wireless network management is accelerating, yet no systematic understanding exists of where these large foundation models outperform lightweight convolutional neural networks (CNNs) for spectrum-related tasks. This paper presents the first diagnostic comparison of VLMs and CNNs for spectrum heatmap understanding in non-terrestrial network and terrestrial network (NTN-TN) cooperative systems. We introduce SpectrumQA, a benchmark comprising 108K visual question-answer pairs across four granularity levels: scene classification (L1), regional reasoning (L2), spatial localization (L3), and semantic reasoning (L4). Our experiments on three NTN-TN scenarios with a frozen Qwen2-VL-7B and a trained ResNet-18 reveal a clear taskdependent complementarity: CNN achieves 72.9% accuracy at severity classification (L1) and 0.552 IoU at spatial localization (L3), while VLM uniquely enables semantic reasoning (L4) with F1=0.576 using only three in-context examples-a capability fundamentally absent in CNN architectures. Chain-of-thought (CoT) prompting further improves VLM reasoning by 12.6% (F1: 0.209->0.233) while having zero effect on spatial tasks, confirming that the complementarity is rooted in architectural differences rather than prompting limitations. A deterministic task-type router that delegates supervised tasks to CNN and reasoning tasks to VLM achieves a composite score of 0.616, a 39.1% improvement over CNN alone. We further show that VLM representations exhibit stronger cross-scenario robustness, with smaller performance degradation in 5 out of 6 transfer directions. These findings provide actionable guidelines: deploy CNNs for spatial localization and VLMs for semantic spectrum reasoning, rather than treating them as substitutes.
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