  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.
