A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection
Abstract
In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://github.com/alipay/VCSL.
Cite
@article{arxiv.2203.02654,
title = {A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection},
author = {Sifeng He and Xudong Yang and Chen Jiang and Gang Liang and Wei Zhang and Tan Pan and Qing Wang and Furong Xu and Chunguang Li and Jingxiong Liu and Hui Xu and Kaiming Huang and Yuan Cheng and Feng Qian and Xiaobo Zhang and Lei Yang},
journal= {arXiv preprint arXiv:2203.02654},
year = {2022}
}
Comments
Accepted by CVPR 2022. Codes are all publicly available at https://github.com/alipay/VCSL