We present an approach for estimating the period with which an action is
repeated in a video. The crux of the approach lies in constraining the period
prediction module to use temporal self-similarity as an intermediate
representation bottleneck that allows generalization to unseen repetitions in
videos in the wild. We train this model, called Repnet, with a synthetic
dataset that is generated from a large unlabeled video collection by sampling
short clips of varying lengths and repeating them with different periods and
counts. This combination of synthetic data and a powerful yet constrained
model, allows us to predict periods in a class-agnostic fashion. Our model
substantially exceeds the state of the art performance on existing periodicity
(PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a new
challenging dataset called Countix (~90 times larger than existing datasets)
which captures the challenges of repetition counting in real-world videos.
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本文介绍了一种利用时间自相似性作为中间表征的方法，通过一定的限制来预测视频中重复动作的周期，结果表明，该模型在现有周期性和重复计数基准测试任务上显著超越现有技术，并在新的数据集 Countix 上显示了鲁棒性。