arXiv 2505.068142025-05-11cs.CVcs.AIcs.CL

NLPCC 2025 共享任务4概览：多模态、多语言与多跳医学教学视频问答挑战赛

Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Engineering, Westlake University（工程学院，西湖大学）

Bin Li, Shenxi Liu, Yixuan Weng, Yue Du, Yuhang Tian, Shoujun Zhou

备注：: 12 pages, 5 figures, 4 tables

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AI总结：

本文介绍NLPCC 2025 M4IVQA挑战赛，设置单视频定位、视频语料检索和语料库定位三个赛道，推动面向医学教学视频的多模态、多语言、多跳问答研究。

摘要：

继第一届（NLPCC 2023 佛山）CMIVQA和第二届（NLPCC 2024 杭州）MMIVQA挑战赛成功举办之后，今年引入了一项新任务，以进一步推进多模态、多语言和多跳医学教学问答（M4IVQA）系统研究，并特别聚焦于医学教学视频。M4IVQA挑战赛侧重于评估能够整合来自医学教学视频的信息、理解多种语言，并回答需要跨各种模态进行推理的多跳问题的模型。该任务包括三个赛道：多模态、多语言和多跳单视频时间答案定位（M4TAGSV），多模态、多语言和多跳视频语料库检索（M4VCR），以及多模态、多语言和多跳视频语料库时间答案定位（M4TAGVC）。M4IVQA的参赛者需要开发能够同时处理视频和文本数据、理解多语言查询，并针对多跳医学问题提供相关答案的算法。我们相信，新推出的M4IVQA挑战赛将推动医疗场景多模态推理系统的创新，最终为多语言社区中更智能的应急响应系统和更有效的医学教育平台作出贡献。我们的官方网站是 https://cmivqa.github.io/

英文摘要：

Following the successful hosts of the 1-st (NLPCC 2023 Foshan) CMIVQA and the 2-rd (NLPCC 2024 Hangzhou) MMIVQA challenges, this year, a new task has been introduced to further advance research in multi-modal, multilingual, and multi-hop medical instructional question answering (M4IVQA) systems, with a specific focus on medical instructional videos. The M4IVQA challenge focuses on evaluating models that integrate information from medical instructional videos, understand multiple languages, and answer multi-hop questions requiring reasoning over various modalities. This task consists of three tracks: multi-modal, multilingual, and multi-hop Temporal Answer Grounding in Single Video (M4TAGSV), multi-modal, multilingual, and multi-hop Video Corpus Retrieval (M4VCR) and multi-modal, multilingual, and multi-hop Temporal Answer Grounding in Video Corpus (M4TAGVC). Participants in M4IVQA are expected to develop algorithms capable of processing both video and text data, understanding multilingual queries, and providing relevant answers to multi-hop medical questions. We believe the newly introduced M4IVQA challenge will drive innovations in multimodal reasoning systems for healthcare scenarios, ultimately contributing to smarter emergency response systems and more effective medical education platforms in multilingual communities. Our official website is https://cmivqa.github.io/

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。