  Repetitive action counting quantifies the frequency of specific actions
performed by individuals. However, existing action-counting datasets have
limited action diversity, potentially hampering model performance on unseen
actions. To address this issue, we propose a framework called First Cycle
Annotated Repetitive Action Counting (FCA-RAC). This framework contains 4
parts: 1) a labeling technique that annotates each training video with the
start and end of the first action cycle, along with the total action count.
This technique enables the model to capture the correlation between the initial
action cycle and subsequent actions; 2) an adaptive sampling strategy that
maximizes action information retention by adjusting to the speed of the first
annotated action cycle in videos; 3) a Multi-Temporal Granularity Convolution
(MTGC) module, that leverages the muli-scale first action as a kernel to
convolve across the entire video. This enables the model to capture action
variations at different time scales within the video; 4) a strategy called
Training Knowledge Augmentation (TKA) that exploits the annotated first action
cycle information from the entire dataset. This allows the network to harness
shared characteristics across actions effectively, thereby enhancing model
performance and generalizability to unseen actions. Experimental results
demonstrate that our approach achieves superior outcomes on RepCount-A and
related datasets, highlighting the efficacy of our framework in improving model
performance on seen and unseen actions. Our paper makes significant
contributions to the field of action counting by addressing the limitations of
existing datasets and proposing novel techniques for improving model
generalizability.
