VLMs and CNNs complement each other on spectrum tasks, with CNNs strong on spatial localization and VLMs on semantic reasoning; a router combining them improves composite performance by 39% over CNN alone.
Seeing radio: From zero RF priors to explainable modulation recognition with vision language models
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VLMs trained on synthetic RF spectrograms generalize to real signals for physical attribute extraction but lack reliable semantic grounding without additional priors.
citing papers explorer
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When Does Multimodal AI Help? Diagnostic Complementarity of Vision-Language Models and CNNs for Spectrum Management in Satellite-Terrestrial Networks
VLMs and CNNs complement each other on spectrum tasks, with CNNs strong on spatial localization and VLMs on semantic reasoning; a router combining them improves composite performance by 39% over CNN alone.
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RF-Analyzer: Can Vision-Language Models Learn RF Understanding from Synthetic Data?
VLMs trained on synthetic RF spectrograms generalize to real signals for physical attribute extraction but lack reliable semantic grounding without additional priors.