战术战伤救护的教义基础视觉问答数据集
A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care
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中文总结 AI 辅助
针对战术战伤救护（TC3）场景，构建了基于教义的视觉问答数据集TC3-VQA，含581个项目、1860个问题，用于训练和评估视觉-语言模型的干预识别、教义回忆及弃权（不执行）能力。
中文摘要 AI 辅助
战术战伤救护（TC3）要求救护人员将伤情和干预措施的视觉观察与既定的临床指南联系起来。开发支持这一过程的视觉-语言模型，需要将可见证据与可追溯的教义相联系的监督信号。我们提出了TC3-VQA，一个由公开的教学和野战TC3视频以及权威TC3文档构建的数据集。该数据集包含581个项目，涵盖11个概念，共1860个问题，涉及干预识别、教义、临床推理、程序指导以及在视觉信息不足时的弃权（不执行）。基于教义的答案保留了原文段落和字符偏移。构建过程结合了视觉标注、段落检索、蕴含检查和跨模型家族的验证。设备箱、解剖标签、时间片段和源元数据伴随问答对。自动审计和两名医生及两名医学生的评分表征了标注质量，其中88个保留项目有人类评分。该数据集为将视觉-语言模型适应于TC3、研究视觉证据与临床知识之间的联系，以及评估识别、教义回忆和弃权（不执行）提供了资源。
英文摘要
Tactical Combat Casualty Care (TC3) requires responders to connect visual observations of injuries and interventions with established clinical guidance. Developing vision-language models to support this process requires supervision that links visible evidence to traceable doctrine. We present TC3-VQA, a dataset constructed from public instructional and field TC3 videos and authoritative TC3 documents. It contains 581 items spanning 11 concepts, with 1,860 questions covering intervention recognition, doctrine, clinical reasoning, procedural guidance, and refusal when visual information is insufficient. Doctrine-based answers preserve verbatim source passages and character offsets. Construction combines visual annotation, passage retrieval, entailment checks, and verification across model families. Equipment boxes, anatomical labels, temporal segments, and source metadata accompany the question-answer pairs. Automated audits and ratings by two physicians and two medical students characterize annotation quality, with human ratings available for 88 retained items. The dataset provides a resource for adapting vision-language models to TC3, studying the connection between visual evidence and clinical knowledge, and evaluating recognition, doctrine recall, and abstention.
发表机构
- Duke University（杜克大学）
- National Yang Ming Chiao Tung University（国立阳明交通大学）
- Korea Military Academy（韩国陆军军官学校）
机构由 AI 辅助整理，请以论文原文为准。
