TRECVID 2022 视频检索任务评估综述
An overview on the evaluated video retrieval tasks at TRECVID 2022
AI总结：
本文综述了TRECVID 2022的六项视频检索评估任务，涵盖数据集、评估框架及35个团队的参与情况，旨在促进基于内容的视频检索技术发展。
摘要：
TREC 视频检索评估（TRECVID）是一项 TREC 风格（文本检索会议风格）的视频分析和检索评估，其目标是通过基于计量学支持的开放式、任务型评估，促进基于内容的数字视频信息开发与检索的研究和进展。在过去二十一年中，这项工作加深了人们对系统如何有效完成此类处理以及如何可靠地对其性能进行基准测试的理解。TRECVID 已由 NIST（美国国家标准与技术研究院）和其他美国政府机构资助。此外，全球许多组织和个人贡献了大量的时间和精力。TRECVID 2022 计划了以下六项任务：即席视频搜索、视频到文本字幕生成、灾难场景描述和索引、扩展视频中的活动识别、深度视频理解以及电影摘要生成。今年，来自全球各研究机构的共 35 个团队报名参加了该评估活动。本文介绍了这些任务、所使用的数据集、评估框架和指标，以及高层次的结果概述。
英文摘要：
The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, tasks-based evaluation supported by metrology. Over the last twenty-one years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2022 planned for the following six tasks: Ad-hoc video search, Video to text captioning, Disaster scene description and indexing, Activity in extended videos, deep video understanding, and movie summarization. In total, 35 teams from various research organizations worldwide signed up to join the evaluation campaign this year. This paper introduces the tasks, datasets used, evaluation frameworks and metrics, as well as a high-level results overview.
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。
