MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality
  Assessment
Kanglei Zhou Liyuan Wang Xingxing Zhang Hubert P. H. Shum Frederick W. B. Li Jianguo Li Xiaohui Liang
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.