MLLMs achieve zero-shot recognition of seizure semiological features better than fine-tuned vision models on most tested features, with signal enhancement and faithful explanations.
Learning transferable visual models from natural language supervi- sion,
2 Pith papers cite this work. Polarity classification is still indexing.
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A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.
citing papers explorer
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Can Multimodal Large Language Models Understand Pathologic Movements? A Pilot Study on Seizure Semiology
MLLMs achieve zero-shot recognition of seizure semiological features better than fine-tuned vision models on most tested features, with signal enhancement and faithful explanations.
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Language-Guided Multimodal Texture Authoring via Generative Models
A language-driven system generates semantically consistent multimodal textures from text prompts by linking autoregressive haptic models and diffusion-based visuals through a shared latent representation.