  Given an untrimmed video, repetitive actions counting aims to estimate the
number of repetitions of class-agnostic actions. To handle the various length
of videos and repetitive actions, also optimization challenges in end-to-end
video model training, down-sampling is commonly utilized in recent
state-of-the-art methods, leading to ignorance of several repetitive samples.
In this paper, we attempt to understand repetitive actions from a full temporal
resolution view, by combining offline feature extraction and temporal
convolution networks. The former step enables us to train repetition counting
network without down-sampling while preserving all repetition regardless of the
video length and action frequency, and the later network models all frames in a
flexible and dynamically expanding temporal receptive field to retrieve all
repetitions with a global aspect. We experimentally demonstrate that our method
achieves better or comparable performance in three public datasets, i.e.,
TransRAC, UCFRep and QUVA. We expect this work will encourage our community to
think about the importance of full temporal resolution.
