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In this paper, we introduce VCSL (Video Copy Segment Localization), a newcomprehensive segment-level annotated video copy dataset. Compared withexisting copy detection datasets restricted by either video-level annotation orsmall-scale, VCSL not only has two orders of magnitude more segment-levellabelled data, with 160k realistic video copy pairs containing more than 280klocalized copied segment pairs, but also covers a variety of video categoriesand a wide range of video duration. All the copied segments inside eachcollected video pair are manually extracted and accompanied by preciselyannotated starting and ending timestamps. Alongside the dataset, we alsopropose a novel evaluation protocol that better measures the predictionaccuracy of copy overlapping segments between a video pair and shows improvedadaptability in different scenarios. By benchmarking several baseline andstate-of-the-art segment-level video copy detection methods with the proposeddataset and evaluation metric, we provide a comprehensive analysis thatuncovers the strengths and weaknesses of current approaches, hoping to open uppromising directions for future works. The VCSL dataset, metric and benchmarkcodes are all publicly available at https://github.com/alipay/VCSL.

A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection