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Visual Question Answering (VQA) models aim to answer natural languagequestions about given images. Due to its ability to ask questions that differfrom those used when training the model, medical VQA has received substantialattention in recent years. However, existing medical VQA models typically focuson answering questions that refer to an entire image rather than where therelevant content may be located in the image. Consequently, VQA models arelimited in their interpretability power and the possibility to probe the modelabout specific image regions. This paper proposes a novel approach for medicalVQA that addresses this limitation by developing a model that can answerquestions about image regions while considering the context necessary to answerthe questions. Our experimental results demonstrate the effectiveness of ourproposed model, outperforming existing methods on three datasets. Our code anddata are available at https://github.com/sergiotasconmorales/locvqa.

Localized Questions in Medical Visual Question Answering