Problem
Research questions and friction points this paper is trying to address.
Repetitive Action Counting
Self-supervised Learning
Temporal Repetition Structure
TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting
🤖 AI Summary
本文提出TReViS框架，通过无标签视频合成解决重复动作计数问题，无需密集时间标注，利用自相似矩阵估计和生成具有真实重复模式的训练序列。
📝 Abstract
Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS, a self-supervised video synthesis framework that enables training RAC models without any repetition labels. TReViS estimates the underlying temporal repetition structure of an unlabeled video via a Temporal Self-Similarity Matrix, infers its cycle statistics, and synthesizes new training sequences that preserve realistic repetition patterns while introducing controlled temporal variability. These synthesized videos are paired with pseudo-labels and used to train existing RAC architectures from scratch. Across multiple datasets and backbones, TReViS consistently outperforms prior self-supervised methods and achieves performance competitive with several supervised baselines, while remaining fully label-free, demonstrating the effectiveness of structure-aware video synthesis for label-free RAC. The source code is available at https://github.com/yfqi/TReViS.
Innovation
Methods, ideas, or system contributions that make the work stand out.
self-supervised
video synthesis
temporal repetition structure
pseudo-labels
repetitive action counting
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