  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.
