# LOGO: A Long-Form Video Dataset for Group Action Quality Assessment

arXiv: https://arxiv.org/abs/2404.05029 · HTML: https://mlsanity.com/paper/2404.05029/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 59 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 30 of 33)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis](https://mlsanity.com/paper/1903.02874/) (2019)
- [FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding](https://mlsanity.com/paper/2004.06704/) (2020)
- [ASFormer: Transformer for Action Segmentation](https://mlsanity.com/paper/2110.08568/) (2021)
- [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)
- [Hierarchical Deep Temporal Models for Group Activity Recognition](https://mlsanity.com/paper/1607.02643/) (2016)
- [Learning To Score Olympic Events](https://mlsanity.com/paper/1611.05125/) (2016)
- [Alleviating Over-segmentation Errors by Detecting Action Boundaries](https://mlsanity.com/paper/2007.06866/) (2020)
- [Actor-Transformers for Group Activity Recognition](https://mlsanity.com/paper/2003.12737/) (2020)
- [MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions](https://mlsanity.com/paper/2105.07404/) (2021)
- [Learning Actor Relation Graphs for Group Activity Recognition](https://mlsanity.com/paper/1904.10117/) (2019)
- [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)
- [Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation](https://mlsanity.com/paper/2003.02824/) (2020)
- [Learning to score the figure skating sports videos](https://mlsanity.com/paper/1802.02774/) (2018)
- [The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos](https://mlsanity.com/paper/1812.05538/) (2018)
- [GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer](https://mlsanity.com/paper/2108.12630/) (2021)
- [Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition](https://mlsanity.com/paper/1611.09078/) (2016)
- [Am I a Baller? Basketball Performance Assessment from First-Person Videos](https://mlsanity.com/paper/1611.05365/) (2016)
- [Joint Inference of Groups, Events and Human Roles in Aerial Videos](https://mlsanity.com/paper/1505.05957/) (2015)
- [Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment](https://mlsanity.com/paper/1702.07772/) (2017)
- [Detecting events and key actors in multi-person videos](https://mlsanity.com/paper/1511.02917/) (2015)

## Built on by (most cited first, 30 of 31)

- [Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning](https://mlsanity.com/paper/2508.04416/) (2025)
- [EgoExo-Fitness: Towards Egocentric and Exocentric Full-Body Action Understanding](https://mlsanity.com/paper/2406.08877/) (2024)
- [GAIA: Rethinking Action Quality Assessment for AI-Generated Videos](https://mlsanity.com/paper/2406.06087/) (2024)
- [FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment](https://mlsanity.com/paper/2405.06887/) (2024)
- [Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision](https://mlsanity.com/paper/2407.06189/) (2024)
- [A Comprehensive Survey of Action Quality Assessment: Method and Benchmark](https://mlsanity.com/paper/2412.11149/) (2024)
- [Narrative Action Evaluation with Prompt-Guided Multimodal Interaction](https://mlsanity.com/paper/2404.14471/) (2024)
- [BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation](https://mlsanity.com/paper/2503.20781/) (2025)
- [Video Action Differencing](https://mlsanity.com/paper/2503.07860/) (2025)
- [PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction](https://mlsanity.com/paper/2505.19972/) (2025)
- [ExAct: A Video-Language Benchmark for Expert Action Analysis](https://mlsanity.com/paper/2506.06277/) (2025)
- [Interpretable Long-term Action Quality Assessment](https://mlsanity.com/paper/2408.11687/) (2024)
- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)
- [TechCoach: Towards Technical-Point-Aware Descriptive Action Coaching](https://mlsanity.com/paper/2411.17130/) (2024)
- [FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment](https://mlsanity.com/paper/2503.23911/) (2025)
- [SkillSight: Efficient First-Person Skill Assessment with Gaze](https://mlsanity.com/paper/2511.19629/) (2025)
- [Meta-CoT: Enhancing Granularity and Generalization in Image Editing](https://mlsanity.com/paper/2604.24625/) (2026)
- [Attention-Driven Multimodal Alignment for Long-term Action Quality Assessment](https://mlsanity.com/paper/2507.21945/) (2025)
- [FLEX: A Largescale Multimodal, Multiview Dataset for Learning Structured Representations for Fitness Action Quality Assessment](https://mlsanity.com/paper/2506.03198/) (2025)
- [HieroAction: Hierarchically Guided VLM for Fine-Grained Action Analysis](https://mlsanity.com/paper/2508.16942/) (2025)
- [SportSkills: Physical Skill Learning from Sports Instructional Videos](https://mlsanity.com/paper/2603.25163/) (2026)
- [FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models](https://mlsanity.com/paper/2608.08736/) (2026)
- [FineSkiing: A Fine-grained Benchmark for Skiing Action Quality Assessment](https://mlsanity.com/paper/2511.10250/) (2025)
- [CaFlow: Enhancing Long-Term Action Quality Assessment with Causal Counterfactual Flow](https://mlsanity.com/paper/2511.21653/) (2025)
- [ProSkill: Segment-Level Skill Assessment in Procedural Videos](https://mlsanity.com/paper/2601.20661/) (2026)
- [SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training](https://mlsanity.com/paper/2603.26400/) (2026)
- [Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training](https://mlsanity.com/paper/2606.28104/) (2026)
- [MoAKE: Toward Unified All-in-One Action Quality Assessment via Mixture of Action Knowledge Experts](https://mlsanity.com/paper/2607.19826/) (2026)
- [Moving Beyond More Views: Redundancy-Aware Ego-Exo Fusion for Proficiency Estimation](https://mlsanity.com/paper/2608.25736/) (2026)
- [MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching](https://mlsanity.com/paper/2608.26094/) (2026)

## Abstract (arXiv)

Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios. However, most existing methods and datasets focus on single-person short-sequence scenes, hindering the application of AQA in more complex situations. To address this issue, we construct a new multi-person long-form video dataset for action quality assessment named LOGO. Distinguished in scenario complexity, our dataset contains 200 videos from 26 artistic swimming events with 8 athletes in each sample along with an average duration of 204.2 seconds. As for richness in annotations, LOGO includes formation labels to depict group information of multiple athletes and detailed annotations on action procedures. Furthermore, we propose a simple yet effective method to model relations among athletes and reason about the potential temporal logic in long-form videos. Specifically, we design a group-aware attention module, which can be easily plugged into existing AQA methods, to enrich the clip-wise representations based on contextual group information. To benchmark LOGO, we systematically conduct investigations on the performance of several popular methods in AQA and action segmentation. The results reveal the challenges our dataset brings. Extensive experiments also show that our approach achieves state-of-the-art on the LOGO dataset. The dataset and code will be released at urlhttps://github.com/shiyi-zh0408/LOGO .