arXiv：cs.AI· Junseob Kim, Jade Chng, Ayman Ali, Victor Moas, Yichun Lee, Po-Chun Chin, Sunil Hwang, Rishikesan Kamaleswaran·· 6 小时前AI 评分35
TC3-VQA：面向战术战伤救治的教条依据视觉问答数据集
A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care
AI 导读
研究者构建了 TC3-VQA 数据集，用于将战术战伤救治（TC3）中的视觉证据与可追溯的临床教条关联起来。该数据集包含 581 个条目、11 个概念和 1,860 个问题，覆盖干预识别、教条、临床推理、流程指导以及视觉信息不足时的拒答。基于教条的答案保留原文逐字段落和字符偏移，并附有设备框、解剖标签、时间片段和来源元数据。
正文
Abstract: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.
| Comments: | 20 pages, 6 figures, 5 tables. Dataset: this https URL code: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.07339 [cs.CV] |
| (or arXiv:2610.07339v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07339 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Junseob Kim [view email]
[v1]
Mon, 5 Oct 2026 20:14:31 UTC (984 KB)
来源：arXiv：cs.AI · arxiv.org