计算机科学

计算机科学 ›› 2022, Vol. 49 ›› Issue (9): 111-122.doi: 10.11896/jsjkx.220500130

• 计算机图形学&多媒体 • 上一篇    下一篇

基于自然语言的视频片段定位综述

聂秀山1, 潘嘉男1, 谭智方1, 刘新放2, 郭杰1, 尹义龙2   

  1. 1 山东建筑大学计算机科学与技术学院 济南 250101
    2 山东大学软件学院 济南 250100
  • 收稿日期:2022-05-16 修回日期:2022-06-10 出版日期:2022-09-15 发布日期:2022-09-09
  • 通讯作者: 聂秀山(niexiushan19@sdjzu.edu.cn)
  • 基金资助:
    国家自然科学基金(62176141);山东省杰出青年基金项目(ZR2021JQ26);山东省泰山学者青年专家项目(tsqn202103088);山东建筑大学特聘教授专项资金

Overview of Natural Language Video Localization

NIE Xiu-shan1, PAN Jia-nan1, TAN Zhi-fang1, LIU Xin-fang2, GUO Jie1, YIN Yi-long2   

  1. 1 School of Computer Science and Technology,Shandong Jianzhu University,Jinan 250101,China
    2 School of Software,Shandong University,Jinan 250100,China
  • Received:2022-05-16 Revised:2022-06-10 Online:2022-09-15 Published:2022-09-09
  • About author:NIE Xiu-shan,Ph.D,professor,Ph.D supervisor,is a senior member of China Computer Federation.His main research interests include machine lear-ning,multimedia and computer vision.
  • Supported by:
    National Natural Science Foundation of China(62176141),Shandong Provincial Natural Science Foundation for Distinguished Young Scholars(ZR2021JQ26),Taishan Scholar Project of Shandong Province(tsqn202103088) and Special Funds for Distinguished Professors of Shandong Jianzhu University.

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摘要： 自然语言视频定位(Natural Language Video Localization,NLVL)是一项新颖而富有挑战性的任务。该任务的目的是根据给定的查询文本从未修剪的视频中找到与这条查询文本语义最为相似的目标片段。与传统的时序动作定位任务不同,NLVL具有更强的灵活性,因为它不受预定义动作列表的限制;同时也更具挑战性,因为NLVL需要从视频和文本两种模态间对齐语义信息。此外,在对齐关系中获取最终的时间戳也是一个艰巨的任务。首先,描述了NLVL的流程;其次,根据是否有监督信息将NLVL算法分为监督方法和弱监督方法两大类并分析其优缺点;然后,总结了常用的数据集和评估指标,对现有的研究进行了总体性能的评估和分析;最后,讨论了技术难点及未来的研究趋势,为今后的工作提供参考。

关键词: 多模态检索, 视频片段定位, 视频理解, 跨模态对齐, 跨模态交互

Abstract: Natural language video localization(NLVL),which aims to locate a target moment from a video that semantically corresponds to a text query,is a novel and challenging task.Different from the task of temporal action localization,NLVL is more flexible without restrictions from predefined action categories.Meanwhile,NLVL is more challenging since it requires align semantic information from both visual and textual modalities.Besides,how to obtain the final timestamp from the alignment relationship is also a tough task.This paper first proposes the pipeline of NLVL,and then categorizes them into supervised and weakly-supervised methods according to whether there is supervised information,following by the analysis of the strengths and weaknesses of each kind of method.Subsequently,the dataset,evaluation protocols and the general performance analysis are presented.Finally,the possible perspectives are obtained by summarizing the existing methods.

Key words: Multimodal retrieval, Video moment localization, Video comprehension, Cross-modal alignment, Cross-modal interaction

中图分类号: 

  • TP391
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