FIVR：细粒度事件视频检索
FIVR: Fine-grained Incident Video Retrieval
摘要：
本文介绍了细粒度事件视频检索（FIVR）问题。给定一个查询视频，目标是检索所有相关的视频，考虑到从重复视频到来自同一事件的视频的几种类型的关联。FIVR提供了一个单一的框架，包含几个检索任务作为特殊情况。为了满足所有这些任务的基准测试需求，我们构建并呈现了一个大规模的注释视频数据集，我们称之为FIVR-200 K，它包括225，960个视频。为了创建数据集，我们设计了一个基于近年来从维基百科抓取的重大新闻事件收集YouTube视频的过程，并部署了一个检索管道，用于根据其估计的适用性作为基准自动选择查询视频。我们还设计了一个协议的数据集的注释相对于四种类型的视频协会定义的FIVR。最后，我们报告了对数据集进行实验研究的结果，比较了基于各种视觉描述符开发的五种最先进的方法，突出了当前问题的挑战。
英文摘要：
This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). 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. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devise a protocol for the annotation of the dataset with respect to the four types of video associations defined by FIVR. Finally, we report the results of an experimental study on the dataset comparing five state-of-the-art methods developed based on a variety of visual descriptors, highlighting the challenges of the current problem.
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