RoMed and CCL: a 144k-question perturbation benchmark for medical VQA and a consistency-plus-contrastive training method that improves LLaVA-Med's accuracy and reduces answer variation.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning
RoMed and CCL: a 144k-question perturbation benchmark for medical VQA and a consistency-plus-contrastive training method that improves LLaVA-Med's accuracy and reduces answer variation.