# Context-aware and Scale-insensitive Temporal Repetition Counting

arXiv: https://arxiv.org/abs/2005.08465 · HTML: https://mlsanity.com/paper/2005.08465/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 30 of 34)

- [TransRAC: Encoding Multi-scale Temporal Correlation with Transformers for Repetitive Action Counting](https://mlsanity.com/paper/2204.01018/) (2022)
- [How Severe is Benchmark-Sensitivity in Video Self-Supervised Learning?](https://mlsanity.com/paper/2203.14221/) (2022)
- [PoseRAC: Pose Saliency Transformer for Repetitive Action Counting](https://mlsanity.com/paper/2303.08450/) (2023)
- [SIGMA: Sinkhorn-Guided Masked Video Modeling](https://mlsanity.com/paper/2407.15447/) (2024)
- [CountLLM: Towards Generalizable Repetitive Action Counting via Large Language Model](https://mlsanity.com/paper/2503.17690/) (2025)
- [Repetitive Action Counting with Hybrid Temporal Relation Modeling](https://mlsanity.com/paper/2412.07233/) (2024)
- [Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization](https://mlsanity.com/paper/2303.11003/) (2023)
- [OVR: A Dataset for Open Vocabulary Temporal Repetition Counting in Videos](https://mlsanity.com/paper/2407.17085/) (2024)
- [Every Shot Counts: Using Exemplars for Repetition Counting in Videos](https://mlsanity.com/paper/2403.18074/) (2024)
- [SMILE: Infusing Spatial and Motion Semantics in Masked Video Learning](https://mlsanity.com/paper/2504.00527/) (2025)
- [Full Resolution Repetition Counting](https://mlsanity.com/paper/2305.13778/) (2023)
- [LocoMotion: Learning Motion-Focused Video-Language Representations](https://mlsanity.com/paper/2410.12018/) (2024)
- [SEVERE++: Evaluating Benchmark Sensitivity in Generalization of Video Representation Learning](https://mlsanity.com/paper/2504.05706/) (2025)
- [Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild](https://mlsanity.com/paper/2312.17330/) (2023)
- [Rethinking temporal self-similarity for repetitive action counting](https://mlsanity.com/paper/2407.09431/) (2024)
- [TrackMAE: Video Representation Learning via Track Mask and Predict](https://mlsanity.com/paper/2603.27268/) (2026)
- [Efficient Action Counting with Dynamic Queries](https://mlsanity.com/paper/2403.01543/) (2024)
- [Count, Decode and Fetch: A New Approach to Handwritten Chinese Character Error Correction](https://mlsanity.com/paper/2307.16253/) (2023)
- [Advancements in Repetitive Action Counting: Joint-Based PoseRAC Model With Improved Performance](https://mlsanity.com/paper/2308.08632/) (2023)
- [Skim then Focus: Integrating Contextual and Fine-grained Views for Repetitive Action Counting](https://mlsanity.com/paper/2406.08814/) (2024)
- [FCA-RAC: First Cycle Annotated Repetitive Action Counting](https://mlsanity.com/paper/2406.12178/) (2024)
- [MultiCounter: Multiple Action Agnostic Repetition Counting in Untrimmed Videos](https://mlsanity.com/paper/2409.04035/) (2024)
- [Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting](https://mlsanity.com/paper/2501.07312/) (2025)
- [Structured-Noise Masked Modeling for Video, Audio and Beyond](https://mlsanity.com/paper/2503.16311/) (2025)
- [Conditional Multi-Event Temporal Grounding in Long-Form Video](https://mlsanity.com/paper/2606.15320/) (2026)
- [Repetitive Activity Counting by Sight and Sound](https://mlsanity.com/paper/2103.13096/) (2021)
- [IVAC-P2L: Leveraging Irregular Repetition Priors for Improving Video Action Counting](https://mlsanity.com/paper/2403.11959/) (2024)
- [GMFL-Net: A Global Multi-geometric Feature Learning Network for Repetitive Action Counting](https://mlsanity.com/paper/2409.00330/) (2024)
- [REST-HANDS: Rehabilitation with Egocentric Vision Using Smartglasses for Treatment of Hands after Surviving Stroke](https://mlsanity.com/paper/2409.20116/) (2024)
- [(LiFT) Lightweight Fitness Transformer: A language-vision model for Remote Monitoring of Physical Training](https://mlsanity.com/paper/2506.06480/) (2025)

## Abstract (arXiv)

Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths. Our approach combines two key insights: (1) Cycle lengths from different actions are unpredictable that require large-scale searching, but, once a coarse cycle length is determined, the variety between repetitions can be overcome by regression. (2) Determining the cycle length cannot only rely on a short fragment of video but a contextual understanding. The first point is implemented by a coarse-to-fine cycle refinement method. It avoids the heavy computation of exhaustively searching all the cycle lengths in the video, and, instead, it propagates the coarse prediction for further refinement in a hierarchical manner. We secondly propose a bidirectional cycle length estimation method for a context-aware prediction. It is a regression network that takes two consecutive coarse cycles as input, and predicts the locations of the previous and next repetitive cycles. To benefit the training and evaluation of temporal repetition counting area, we construct a new and largest benchmark, which contains 526 videos with diverse repetitive actions. Extensive experiments show that the proposed network trained on a single dataset outperforms state-of-the-art methods on several benchmarks, indicating that the proposed framework is general enough to capture repetition patterns across domains.