FIVR: Fine-grained Incident Video Retrieval
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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 association that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains as special cases several retrieval tasks. To address the benchmarking needs of all such tasks, we constructed and present a large-scale annotated video dataset, which we call FIVR-200K and it comprises 225,960 videos. To create the dataset, we devised a process for the collection of YouTube videos based on major events from recent years crawled from Wikipedia and deployed a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devised a protocol for the annotation of the dataset with respect to the four types of video association defined by FIVR. Finally, we report results of an experimental study on the dataset comparing a variety of state-of-the-art visual descriptors and aggregation techniques, highlighting the challenges of the problem at hand.
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