# Uncertainty-aware Score Distribution Learning for Action Quality Assessment

arXiv: https://arxiv.org/abs/2006.07665 · HTML: https://mlsanity.com/paper/2006.07665/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 30 of 80)

- [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)
- [Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition](https://mlsanity.com/paper/2104.00232/) (2021)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports](https://mlsanity.com/paper/2401.01505/) (2024)
- [TSA-Net: Tube Self-Attention Network for Action Quality Assessment](https://mlsanity.com/paper/2201.03746/) (2022)
- [Action Quality Assessment with Temporal Parsing Transformer](https://mlsanity.com/paper/2207.09270/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [LOGO: A Long-Form Video Dataset for Group Action Quality Assessment](https://mlsanity.com/paper/2404.05029/) (2024)
- [GAIA: Rethinking Action Quality Assessment for AI-Generated Videos](https://mlsanity.com/paper/2406.06087/) (2024)
- [Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment](https://mlsanity.com/paper/2202.14019/) (2022)
- [FineParser: A Fine-grained Spatio-temporal Action Parser for Human-centric Action Quality Assessment](https://mlsanity.com/paper/2405.06887/) (2024)
- [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://mlsanity.com/paper/2212.04638/) (2022)
- [An Economic Framework for 6-DoF Grasp Detection](https://mlsanity.com/paper/2407.08366/) (2024)
- [Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive Regression](https://mlsanity.com/paper/2501.03674/) (2025)
- [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)
- [PECoP: Parameter Efficient Continual Pretraining for Action Quality Assessment](https://mlsanity.com/paper/2311.07603/) (2023)
- [Narrative Action Evaluation with Prompt-Guided Multimodal Interaction](https://mlsanity.com/paper/2404.14471/) (2024)
- [Auto-Encoding Score Distribution Regression for Action Quality Assessment](https://mlsanity.com/paper/2111.11029/) (2021)
- [ExpertAF: Expert Actionable Feedback from Video](https://mlsanity.com/paper/2408.00672/) (2024)
- [Piano Skills Assessment](https://mlsanity.com/paper/2101.04884/) (2021)
- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [A Survey of Video-based Action Quality Assessment](https://mlsanity.com/paper/2204.09271/) (2022)
- [Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment](https://mlsanity.com/paper/2403.13798/) (2024)
- [Surgical Skill Assessment via Video Semantic Aggregation](https://mlsanity.com/paper/2208.02611/) (2022)
- [RICA2: Rubric-Informed, Calibrated Assessment of Actions](https://mlsanity.com/paper/2408.02138/) (2024)
- [AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation](https://mlsanity.com/paper/2409.00342/) (2024)
- [Continual Action Assessment via Task-Consistent Score-Discriminative Feature Distribution Modeling](https://mlsanity.com/paper/2309.17105/) (2023)

## Abstract (arXiv)

Assessing action quality from videos has attracted growing attention in recent years. Most existing approaches usually tackle this problem based on regression algorithms, which ignore the intrinsic ambiguity in the score labels caused by multiple judges or their subjective appraisals. To address this issue, we propose an uncertainty-aware score distribution learning (USDL) approach for action quality assessment (AQA). Specifically, we regard an action as an instance associated with a score distribution, which describes the probability of different evaluated scores. Moreover, under the circumstance where fine-grained score labels are available (e.g., difficulty degree of an action or multiple scores from different judges), we further devise a multi-path uncertainty-aware score distributions learning (MUSDL) method to explore the disentangled components of a score. We conduct experiments on three AQA datasets containing various Olympic actions and surgical activities, where our approaches set new state-of-the-arts under the Spearman's Rank Correlation.