远程交互式视频检索评估:在第 10 届视频浏览器对决中比较远程环境下的 16 个交互式视频搜索系统。

IF 3.6 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Multimedia Information Retrieval Pub Date : 2022-01-01 Epub Date: 2022-01-26 DOI:10.1007/s13735-021-00225-2
Silvan Heller, Viktor Gsteiger, Werner Bailer, Cathal Gurrin, Björn Þór Jónsson, Jakub Lokoč, Andreas Leibetseder, František Mejzlík, Ladislav Peška, Luca Rossetto, Konstantin Schall, Klaus Schoeffmann, Heiko Schuldt, Florian Spiess, Ly-Duyen Tran, Lucia Vadicamo, Patrik Veselý, Stefanos Vrochidis, Jiaxin Wu
{"title":"远程交互式视频检索评估:在第 10 届视频浏览器对决中比较远程环境下的 16 个交互式视频搜索系统。","authors":"Silvan Heller, Viktor Gsteiger, Werner Bailer, Cathal Gurrin, Björn Þór Jónsson, Jakub Lokoč, Andreas Leibetseder, František Mejzlík, Ladislav Peška, Luca Rossetto, Konstantin Schall, Klaus Schoeffmann, Heiko Schuldt, Florian Spiess, Ly-Duyen Tran, Lucia Vadicamo, Patrik Veselý, Stefanos Vrochidis, Jiaxin Wu","doi":"10.1007/s13735-021-00225-2","DOIUrl":null,"url":null,"abstract":"<p><p>The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.</p>","PeriodicalId":48501,"journal":{"name":"International Journal of Multimedia Information Retrieval","volume":"11 1","pages":"1-18"},"PeriodicalIF":3.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791088/pdf/","citationCount":"0","resultStr":"{\"title\":\"Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown.\",\"authors\":\"Silvan Heller, Viktor Gsteiger, Werner Bailer, Cathal Gurrin, Björn Þór Jónsson, Jakub Lokoč, Andreas Leibetseder, František Mejzlík, Ladislav Peška, Luca Rossetto, Konstantin Schall, Klaus Schoeffmann, Heiko Schuldt, Florian Spiess, Ly-Duyen Tran, Lucia Vadicamo, Patrik Veselý, Stefanos Vrochidis, Jiaxin Wu\",\"doi\":\"10.1007/s13735-021-00225-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.</p>\",\"PeriodicalId\":48501,\"journal\":{\"name\":\"International Journal of Multimedia Information Retrieval\",\"volume\":\"11 1\",\"pages\":\"1-18\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791088/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Multimedia Information Retrieval\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s13735-021-00225-2\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/1/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Multimedia Information Retrieval","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s13735-021-00225-2","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/1/26 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

视频浏览器对决 "通过年度互动评估活动,吸引专注于互动视频检索的研究团队,解决视频搜索的难题。该活动旨在通过在大型视频库中执行选定的搜索任务来测试参与活动的交互式视频检索系统的性能。2021 年 "视频浏览器对决 "在其十年的历史上首次采用了完全远程的组织方式,并创下了 16 个评分系统参赛的记录。在本文中,我们将介绍比赛设置、任务和结果,并概述参赛系统所使用的最先进方法。通过查看十个系统提供的查询结果日志,我们分析了检索模型性能和正确提交前浏览时间的差异。通过数据收集方法和工具的进步,我们对临时视频搜索任务进行了全面分析,讨论了结果、任务设计和方法挑战。我们强调,几乎所有性能最佳的系统都利用某种联合嵌入技术进行文本-图像检索,并在已知项目搜索的查询中指定时间上下文。这些技术的结合推动了目前表现最出色的系统,同时我们也为交互式视频搜索引擎和视频浏览器对决竞赛本身指出了几个未来的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown.

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
7.80
自引率
5.40%
发文量
36
期刊介绍: Aims and Scope The International Journal of Multimedia Information Retrieval (IJMIR) is a scholarly archival journal publishing original, peer-reviewed research contributions. Its editorial board strives to present the most important research results in areas within the field of multimedia information retrieval. Core areas include exploration, search, and mining in general collections of multimedia consisting of information from the WWW to scientific imaging to personal archives. Comprehensive review and survey papers that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant. Relevant topics include Image and video retrieval - theory, algorithms, and systems Social media interaction and retrieval - collaborative filtering, social voting and ranking Music and audio retrieval - theory, algorithms, and systems Scientific and Bio-imaging - MRI, X-ray, ultrasound imaging analysis and retrieval Semantic learning - visual concept detection, object recognition, and tag learning Exploration of media archives - browsing, experiential computing Interfaces - multimedia exploration, visualization, query and retrieval Multimedia mining - life logs, WWW media mining, pervasive media analysis Interactive search - interactive learning and relevance feedback in multimedia retrieval Distributed and high performance media search - efficient and very large scale search Applications - preserving cultural heritage, 3D graphics models, etc. Editorial Policies: We aim for a fast decision time (less than 4 months for the initial decision) There are no page charges in IJMIR. Papers are published on line in advance of print publication. Academic, industrial researchers, and practitioners involved with multimedia search, exploration, and mining will find IJMIR to be an essential source for important results in the field.
期刊最新文献
Text-assisted attention-based cross-modal hashing Augmented inputs for surveillance re-identification Image enhancement with bi-directional normalization and color attention-guided generative adversarial networks PSNet: position-shift alignment network for image caption Sentiment analysis using deep learning techniques: a comprehensive review
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1