With growing interest in recent years, medical visual question answering (Med-VQA) has rapidly evolved, with multimodal large language models (MLLMs) emerging as an alternative to classical model architectures. Specifically, their ability to add visual information to the input of pre-trained LLMs brings new capabilities for image interpretation. However, simple visual errors cast doubt on the actual visual understanding abilities of these models. To address this, region-based questions have been proposed as a means to assess and enhance actual visual understanding through compositional evaluation. To combine these two perspectives, this paper introduces targeted visual prompting to equip MLLMs with region-based questioning capabilities. By presenting the model with both the isolated region and the region in its context in a customized visual prompt, we show the effectiveness of our method across multiple datasets while comparing it to several baseline models. Our code and data are available at https://github.com/sergiotasconmorales/locvqallm.

本研究解决了医疗视觉问答（Med-VQA）中模型视觉理解能力不足的问题，提出了定向视觉提示的新方法，以提升多模态大型语言模型（MLLMs）在区域性问题上的表现。研究发现，结合孤立区域与上下文区域的定制视觉提示能够显著增强模型的视觉理解能力，展示了该方法在多个数据集上的有效性。

面向医疗视觉问答的定向视觉提示