# MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment

arXiv: https://arxiv.org/abs/2403.04398 · HTML: https://mlsanity.com/paper/2403.04398/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 7 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model](https://mlsanity.com/paper/2303.05118/) (2023)
- [Few-shot Class-incremental Learning for Classification and Object Detection: A Survey](https://mlsanity.com/paper/2308.06764/) (2023)
- [PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation](https://mlsanity.com/paper/2309.02165/) (2023)

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey of Action Quality Assessment: Method and Benchmark](https://mlsanity.com/paper/2412.11149/) (2024)
- [CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment](https://mlsanity.com/paper/2404.13999/) (2024)
- [PHI: Bridging Domain Shift in Long-Term Action Quality Assessment via Progressive Hierarchical Instruction](https://mlsanity.com/paper/2505.19972/) (2025)
- [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)
- [FlyPrompt: Brain-Inspired Random-Expanded Routing with Temporal-Ensemble Experts for General Continual Learning](https://mlsanity.com/paper/2602.01976/) (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)
- [Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition](https://mlsanity.com/paper/2606.13022/) (2026)
- [Latent Visual Diffusion Reasoning with Monte Carlo Tree Search](https://mlsanity.com/paper/2606.27988/) (2026)
- [Two-Stage Multi-Modal Fusion with Adaptive Alignment for Action Quality Assessment](https://mlsanity.com/paper/2607.07438/) (2026)
- [CE4L: Continual Ego, Exo, and Ego-Exo Learning](https://mlsanity.com/paper/2609.23492/) (2026)

## Abstract (arXiv)

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the misalignment between static old features and the dynamically changing feature manifold causes severe catastrophic forgetting. To address this novel problem, we propose Manifold-Aligned Graph Regularization (MAGR), which first aligns deviated old features to the current feature manifold, ensuring representation consistency. It then constructs a graph jointly arranging old and new features aligned with quality scores. Experiments show MAGR outperforms recent strong baselines with up to 6.56%, 5.66%, 15.64%, and 9.05% correlation gains on the MTL-AQA, FineDiving, UNLV-Dive, and JDM-MSA split datasets, respectively. This validates MAGR for continual assessment challenges arising from non-stationary skill variations. Code is available at https://github.com/ZhouKanglei/MAGR_CAQA}{https://github.com/ZhouKanglei/MAGR_CAQA.