# PATS: Proficiency-Aware Temporal Sampling for Multi-View Sports Skill Assessment

arXiv: https://arxiv.org/abs/2506.04996 · HTML: https://mlsanity.com/paper/2506.04996/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 20 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World](https://mlsanity.com/paper/2403.16182/) (2024)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [TSA-Net: Tube Self-Attention Network for Action Quality Assessment](https://mlsanity.com/paper/2201.03746/) (2022)
- [Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs](https://mlsanity.com/paper/2203.03990/) (2022)
- [Multimodal Action Quality Assessment](https://mlsanity.com/paper/2402.09444/) (2024)
- [A Comprehensive Survey of Action Quality Assessment: Method and Benchmark](https://mlsanity.com/paper/2412.11149/) (2024)
- [When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2503.15096/) (2025)
- [egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks](https://mlsanity.com/paper/2502.20879/) (2025)
- [Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition](https://mlsanity.com/paper/2201.10394/) (2022)
- [SkillFormer: Unified Multi-View Video Understanding for Proficiency Estimation](https://mlsanity.com/paper/2505.08665/) (2025)
- [Gate-Shift-Pose: Enhancing Action Recognition in Sports with Skeleton Information](https://mlsanity.com/paper/2503.04470/) (2025)

## Built on by (most cited first, 5 of 5)

- [ProfVLM: A lightweight video-language model for multi-view proficiency estimation](https://mlsanity.com/paper/2509.26278/) (2025)
- [HieroAction: Hierarchically Guided VLM for Fine-Grained Action Analysis](https://mlsanity.com/paper/2508.16942/) (2025)
- [Parameter-Efficient Multi-View Proficiency Estimation: From Discriminative Classification to Generative Feedback](https://mlsanity.com/paper/2605.03848/) (2026)
- [SkillMoV: Mixture-of-View Routing with Prototype-Conditioned Gating for Unified Multi-View Proficiency Estimation](https://mlsanity.com/paper/2606.17615/) (2026)
- [SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos](https://mlsanity.com/paper/2606.31127/) (2026)

## Abstract (arXiv)

Automated sports skill assessment requires capturing fundamental movement patterns that distinguish expert from novice performance, yet current video sampling methods disrupt the temporal continuity essential for proficiency evaluation. To this end, we introduce Proficiency-Aware Temporal Sampling (PATS), a novel sampling strategy that preserves complete fundamental movements within continuous temporal segments for multi-view skill assessment. PATS adaptively segments videos to ensure each analyzed portion contains full execution of critical performance components, repeating this process across multiple segments to maximize information coverage while maintaining temporal coherence. Evaluated on the EgoExo4D benchmark with SkillFormer, PATS surpasses the state-of-the-art accuracy across all viewing configurations (+0.65% to +3.05%) and delivers substantial gains in challenging domains (+26.22% bouldering, +2.39% music, +1.13% basketball). Systematic analysis reveals that PATS successfully adapts to diverse activity characteristics-from high-frequency sampling for dynamic sports to fine-grained segmentation for sequential skills-demonstrating its effectiveness as an adaptive approach to temporal sampling that advances automated skill assessment for real-world applications. Visit our project page at https://edowhite.github.io/PATS