# Learning Sparse Temporal Video Mapping for Action Quality Assessment in Floor Gymnastics

arXiv: https://arxiv.org/abs/2301.06103 · HTML: https://mlsanity.com/paper/2301.06103/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 15 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment](https://mlsanity.com/paper/2204.03646/) (2022)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [Learning To Score Olympic Events](https://mlsanity.com/paper/1611.05125/) (2016)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)
- [Action Quality Assessment Across Multiple Actions](https://mlsanity.com/paper/1812.06367/) (2018)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Learning to score the figure skating sports videos](https://mlsanity.com/paper/1802.02774/) (2018)
- [SPAct: Self-supervised Privacy Preservation for Action Recognition](https://mlsanity.com/paper/2203.15205/) (2022)
- [Am I a Baller? Basketball Performance Assessment from First-Person Videos](https://mlsanity.com/paper/1611.05365/) (2016)
- [TSA-Net: Tube Self-Attention Network for Action Quality Assessment](https://mlsanity.com/paper/2201.03746/) (2022)
- [Action Quality Assessment using Siamese Network-Based Deep Metric Learning](https://mlsanity.com/paper/2002.12096/) (2020)
- [On-field player workload exposure and knee injury risk monitoring via deep learning](https://mlsanity.com/paper/1809.08016/) (2018)
- [Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning](https://mlsanity.com/paper/1903.07221/) (2019)

## Built on by (most cited first, 9 of 9)

- [SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2410.08474/) (2024)
- [A Comprehensive Survey of Action Quality Assessment: Method and Benchmark](https://mlsanity.com/paper/2412.11149/) (2024)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)
- [FineSkiing: A Fine-grained Benchmark for Skiing Action Quality Assessment](https://mlsanity.com/paper/2511.10250/) (2025)
- [KD-Judge: A Knowledge-Driven Automated Judge Framework for Functional Fitness Movements on Edge Devices](https://mlsanity.com/paper/2604.19834/) (2026)
- [SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing](https://mlsanity.com/paper/2604.23392/) (2026)
- [MLCR: Multi-Level Cue Refinement for Long-Term Multimodal Action Quality Assessment](https://mlsanity.com/paper/2605.08945/) (2026)
- [Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training](https://mlsanity.com/paper/2606.28104/) (2026)

## Abstract (arXiv)

Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datasets mainly incorporate sports where the performance lasts only a few seconds. Consequently, state-ofthe-art sports quality assessment methods specifically focus on spatial structure. Although they achieve high performance in short-term sports, they are unable to model prolonged video sequences and fail to achieve similar performance in long-term sports. To facilitate such analysis, we introduce a new dataset, coined AGF-Olympics, that incorporates artistic gymnastic floor routines. AFG-Olympics provides highly challenging scenarios with extensive background, viewpoint, and scale variations over an extended sample duration of up to 2 minutes. In addition, we propose a discriminative attention module to map the dense feature space into a sparse representation by disentangling complex associations. Extensive experiments indicate that our proposed module provides an effective way to embed long-range spatial and temporal correlation semantics.