Public articles linked to the same research event.
arXiv ZeroMAG introduces a zero-shot multimodal adapter generation framework that, with a frozen EEG encoder and prediction head, uses only unlabeled target calibration recordings and task context to generate adapter weights through a configuration-invariant 1+N adapter, a modality–subject–task condition, and a function-constrained latent space learned from source adapters; across six held-out target datasets and three EEG foundation model backbones it improves balanced accuracy by 7.22 percentage points over EEG-only inference and 4.89 points over direct weight regression, coming within 0.50 points of supervised multimodal adaptation on average, with no target labels or target-side optimization.
ZeroMAG introduces a zero-shot multimodal adapter generation framework that, with a frozen EEG encoder and prediction head, uses only unlabeled target calibration recordings and task context to generate adapter weights through a configuration-invariant 1+N adapter, a modality–subject–task condition, and a function-constrained latent space learned from source adapters; across six held-out target datasets and three EEG foundation model backbones it improves balanced accuracy by 7.22 percentage points over EEG-only inference and 4.89 points over direct weight regression, coming within 0.50 points of supervised multimodal adaptation on average, with no target labels or target-side optimization.
ZeroMAG introduces a zero-shot multimodal adapter generation framework that, with a frozen EEG encoder and prediction head, uses only unlabeled target calibration recordings and task context to generate adapter weights through a configuration-invariant 1+N adapter, a modality–subject–task condition, and a function-constrained latent space learned from source adapters; across six held-out target datasets and three EEG foundation model backbones it improves balanced accuracy by 7.22 percentage points over EEG-only inference and 4.89 points over direct weight regression, coming within 0.50 points of supervised multimodal adaptation on average, with no target labels or target-side optimization.
ZeroMAG introduces a zero-shot multimodal adapter generation framework that, with a frozen EEG encoder and prediction head, uses only unlabeled target calibration recordings and task context to generate adapter weights through a configuration-invariant 1+N adapter, a modality–subject–task condition, and a function-constrained latent space learned from source adapters; across six held-out target datasets and three EEG foundation model backbones it improves balanced accuracy by 7.22 percentage points over EEG-only inference and 4.89 points over direct weight regression, coming within 0.50 points of supervised multimodal adaptation on average, with no target labels or target-side optimization.