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.

我们提出了一个名为首轮注释重复动作计数（FCA-RAC）的框架，通过标记技术、自适应采样策略、多时序颗粒度卷积（MTGC）模块和训练知识增强（TKA）策略，有效改善了现有数据集的局限性，提高了模型的泛化性能和推广到未知动作的能力。

FCA-RAC: 首周期注释重复动作计数

FCA-RAC: First Cycle Annotated Repetitive Action Counting

Counting repetitive actions are widely seen in human activities such as
physical exercise. Existing methods focus on performing repetitive action
counting in short videos, which is tough for dealing with longer videos in more
realistic scenarios. In the data-driven era, the degradation of such
generalization capability is mainly attributed to the lack of long video
datasets. To complement this margin, we introduce a new large-scale repetitive
action counting dataset covering a wide variety of video lengths, along with
more realistic situations where action interruption or action inconsistencies
occur in the video. Besides, we also provide a fine-grained annotation of the
action cycles instead of just counting annotation along with a numerical value.
Such a dataset contains 1,451 videos with about 20,000 annotations, which is
more challenging. For repetitive action counting towards more realistic
scenarios, we further propose encoding multi-scale temporal correlation with
transformers that can take into account both performance and efficiency.
Furthermore, with the help of fine-grained annotation of action cycles, we
propose a density map regression-based method to predict the action period,
which yields better performance with sufficient interpretability. Our proposed
method outperforms state-of-the-art methods on all datasets and also achieves
better performance on the unseen dataset without fine-tuning. The dataset and
code are available.

为解决现有短视频针对长视频中多样化和实际情况不足的问题，本文提出了一种包含多尺度时间关联的 transformer 编码方法和基于密度图回归的行动周期预测方法，并提供了更细致的注释来解决重复行动计数过程中的不一致现象。该方法不仅在所有数据集上均优于现有方法，而且在未经微调的未见过数据集上也表现更好。