Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful features to the medical VQA task. Our proposed method is designed to increase meta-data by auto-annotation, deal with noisy labels, and output meta-models which provide robust features for medical VQA tasks. Extensively experimental results on two public medical VQA datasets show that our approach achieves superior accuracy in comparison with other state-of-the-art methods, while does not require external data to train meta-models.

翻译：转移学习是提取有意义的特征和克服医疗视觉问答任务中数据限制的重要一步,然而,现有的医学视觉问答方法大多依靠外部数据进行转移学习,而数据集中的元数据没有得到充分利用。在本文件中,我们提出了一个新的多种元模型量化方法,有效学习元注解,并利用医学视频问答任务中有意义的特征。我们提议的方法旨在通过自动注解增加元数据,处理吵闹标签和产出元模型,为医疗视频问答任务提供强有力的特征。两个公共医学 VQA数据集的广泛实验结果表明,我们的方法与其他最先进的方法相比,实现了更高的准确性,而不需要外部数据来培训元模型。