InVID FIVR-200K
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The InVID FIVR-200K dataset has been developed in the context of the InVID project with the aim of simulating the problem of Fine-grained Incident Video Retrieval (FIVR). FIVR is the problem where: given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. To address the benchmarking needs of such problem, the large-scale video dataset FIVR-200K has been constructed. It comprises 225,960 YouTube videos collected based on 4,687 major news events crawled from Wikipedia, and 100 video queries selected based on an automatic selection process. For the annotation of the dataset, an annotation protocol has been devised with respect to four types of video associations, i.e., Near-Duplicate Videos (ND), Duplicate Scene Videos (DS), Complementary Scene Videos (CS), and Incident Scene Videos (IS). To this end, FIVR-200K dataset contains the list of the collected Youtube ids, the crawled events from Wikipedia and the video annotations, which include the set of videos for each associations type for each query in the dataset.
InVID FIVR-200K数据集依托InVID项目研发，旨在模拟细粒度事件视频检索（Fine-grained Incident Video Retrieval, FIVR）问题。细粒度事件视频检索任务的定义为：给定查询视频，目标是检索出所有关联视频，关联类型涵盖从重复视频到同事件相关视频等多个类别。为满足该任务的基准测试需求，研究人员构建了大规模视频数据集FIVR-200K。该数据集包含基于从维基百科爬取的4687个重大新闻事件收集的225960条YouTube视频，以及通过自动筛选流程选出的100个视频查询。为完成数据集标注，研究人员针对四类视频关联类型设计了标注规范，分别为近似重复视频（Near-Duplicate Videos, ND）、重复场景视频（Duplicate Scene Videos, DS）、互补场景视频（Complementary Scene Videos, CS）以及事件场景视频（Incident Scene Videos, IS）。FIVR-200K数据集包含收集到的YouTube视频ID列表、从维基百科爬取的事件信息，以及视频标注数据，其中标注数据涵盖了数据集中每个查询对应的各类关联类型的视频集合。
