  This paper presents a significant contribution to the field of repetitive
action counting through the introduction of a new approach called Pose Saliency
Representation. The proposed method efficiently represents each action using
only two salient poses instead of redundant frames, which significantly reduces
the computational cost while improving the performance. Moreover, we introduce
a pose-level method, PoseRAC, which is based on this representation and
achieves state-of-the-art performance on two new version datasets by using Pose
Saliency Annotation to annotate salient poses for training. Our lightweight
model is highly efficient, requiring only 20 minutes for training on a GPU, and
infers nearly 10x faster compared to previous methods. In addition, our
approach achieves a substantial improvement over the previous state-of-the-art
TransRAC, achieving an OBO metric of 0.56 compared to 0.29 of TransRAC. The
code and new dataset are available at https://github.com/MiracleDance/PoseRAC
for further research and experimentation, making our proposed approach highly
accessible to the research community.
