  Video Action Counting (VAC) is crucial in analyzing sports, fitness, and
everyday activities by quantifying repetitive actions in videos. However,
traditional VAC methods have overlooked the complexity of action repetitions,
such as interruptions and the variability in cycle duration. Our research
addresses the shortfall by introducing a novel approach to VAC, called
Irregular Video Action Counting (IVAC). IVAC prioritizes modeling irregular
repetition patterns in videos, which we define through two primary aspects:
Inter-cycle Consistency and Cycle-interval Inconsistency. Inter-cycle
Consistency ensures homogeneity in the spatial-temporal representations of
cycle segments, signifying action uniformity within cycles. Cycle-interval
inconsistency highlights the importance of distinguishing between cycle
segments and intervals based on their inherent content differences. To
encapsulate these principles, we propose a new methodology that includes
consistency and inconsistency modules, supported by a unique pull-push loss
(P2L) mechanism. The IVAC-P2L model applies a pull loss to promote coherence
among cycle segment features and a push loss to clearly distinguish features of
cycle segments from interval segments. Empirical evaluations conducted on the
RepCount dataset demonstrate that the IVAC-P2L model sets a new benchmark in
VAC task performance. Furthermore, the model demonstrates exceptional
adaptability and generalization across various video contents, outperforming
existing models on two additional datasets, UCFRep and Countix, without the
need for dataset-specific optimization. These results confirm the efficacy of
our approach in addressing irregular repetitions in videos and pave the way for
further advancements in video analysis and understanding.
