Skim then Focus: Integrating Contextual and Fine-grained Views for Repetitive Action Counting
<|reference_start|>Skim then Focus: Integrating Contextual and Fine-grained Views for Repetitive Action Counting: 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 typically engage in coarse skimming of entire sequences to grasp the general action pattern initially, followed by a finer, frame-by-frame focus to determine if it aligns with the target action. 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 the robustness of our method. 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.<|reference_end|>
@article{zhao2024skim,
    title={Skim then Focus: Integrating Contextual and Fine-grained Views for
  Repetitive Action Counting},
    author={Zhengqi Zhao, Xiaohu Huang, Hao Zhou, Kun Yao, Errui Ding, Jingdong
  Wang, Xinggang Wang, Wenyu Liu, Bin Feng},
    journal={arXiv preprint arXiv:2406.08814},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08814},
    primaryClass={cs.CV}
}