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We present an approach for estimating the period with which an action isrepeated in a video. The crux of the approach lies in constraining the periodprediction module to use temporal self-similarity as an intermediaterepresentation bottleneck that allows generalization to unseen repetitions invideos in the wild. We train this model, called Repnet, with a syntheticdataset that is generated from a large unlabeled video collection by samplingshort clips of varying lengths and repeating them with different periods andcounts. This combination of synthetic data and a powerful yet constrainedmodel, allows us to predict periods in a class-agnostic fashion. Our modelsubstantially exceeds the state of the art performance on existing periodicity(PERTUBE) and repetition counting (QUVA) benchmarks. We also collect a newchallenging dataset called Countix (~90 times larger than existing datasets)which captures the challenges of repetition counting in real-world videos.Project webpage: https://sites.google.com/view/repnet .

Counting Out Time: Class Agnostic Video Repetition Counting in the Wild