S. Bit

Gradient-guided adapter merging for neuroimaging vision-language models

GRAM is presented, a layer-selective low-rank adaptation (LoRA)-based fine-tuning and merging framework for multi-task neuroimaging visual question-answering (VQA), which uses a gradient ratio that contrasts class-specific gradients to identify task-discriminative layers.