fMRI-LM builds a foundation model that aligns fMRI signals with language through tokenization, LLM adaptation, and instruction tuning to enable semantic understanding of brain activity.
arXiv preprint arXiv:2507.07796 , year=
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FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.
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fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding
fMRI-LM builds a foundation model that aligns fMRI signals with language through tokenization, LLM adaptation, and instruction tuning to enable semantic understanding of brain activity.
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Text-Guided Multi-Scale Frequency Representation Adaptation
FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.