Skim then Focus: Integrating Contextual and Fine-grained Views for Repetitive Action Counting

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Zhengqi Zhao, Xiaohu Huang, Hao Zhou, Kun Yao, Errui Ding, Jingdong Wang, Xinggang Wang, Wenyu Liu, Bin Feng

Published: 2025, Last Modified: 27 Jan 2026Int. J. Comput. Vis. 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The key to action counting is accurately locating each video’s repetitive actions. Instead of estimating the probability of each frame belonging to an action directly, we propose a dual-branch network, i.e., SkimFocusNet, working in a two-step manner. The model draws inspiration from empirical observations indicating that humans initially engage in coarse skimming of entire sequences to quickly locate potential target action frames and grasp general motion patterns. This is followed by finer, frame-by-frame focusing to precisely determine whether the located frames align with the target actions. Specifically, SkimFocusNet incorporates a skim branch and a focus branch. The skim branch scans the global contextual information throughout the sequence to identify potential target action for guidance. Subsequently, the focus branch utilizes the guidance to diligently identify repetitive actions using a long-short adaptive guidance (LSAG) block. Additionally, we have observed that videos in existing datasets often feature only one type of repetitive action, which inadequately represents real-world scenarios. To more accurately describe real-life situations, we establish the Multi-RepCount dataset, which includes videos containing multiple repetitive motions. On Multi-RepCount, our SkimFoucsNet can perform specified action counting, that is, to enable counting a particular action type by referencing an exemplary video. This capability substantially exhibits our method’s robustness, particularly in accurately performing action counting despite the presence of interfering actions. Extensive experiments demonstrate that SkimFocusNet achieves state-of-the-art performances with significant improvements. We also conduct a thorough ablation study to evaluate the network components. The source code will be published upon acceptance https://github.com/isotopezzq/SkimFocusNet.