Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation

arXiv:2403.02707v1 Announce Type: new
Abstract: Leveraging pre-trained visual language models has become a widely adopted approach for improving performance in downstream visual question answering (VQA) applications. However, in the specialized field of medical VQA, the scarcity of available data poses a significant barrier to achieving reliable model generalization. Numerous methods have been proposed to enhance model generalization, addressing the issue from data-centric and model-centric perspectives. Data augmentation techniques are commonly employed to enrich the dataset, while various regularization approaches aim …

abstract applications arxiv become cs.cv cs.mm data gradient language language models medical model generalization performance question question answering tasks type via visual vqa