BriMA：基于多模态适应的持续动作质量评估
BriMA: Bridged Modality Adaptation for Multi-Modal Continual Action Quality Assessment
AI总结：
BriMA通过桥接模态适应方法，在模态缺失条件下提升多模态持续动作质量评估的性能。
摘要：
动作质量评估（AQA）旨在评分动作的完成质量，广泛应用于体育分析、康复评估和人类技能评估。多模态AQA最近通过利用互补的视觉和运动学线索取得了显著进展，但现实部署中常常面临非平稳的模态不平衡问题，某些模态因传感器故障或标注缺口而缺失或间歇性不可用。现有持续AQA方法忽视了这一问题，假设所有模态在训练过程中始终保持完整和稳定，限制了其实际应用。为了解决这一挑战，我们引入了桥接模态适应（BriMA），一种创新的多模态持续AQA方法，在模态缺失条件下进行。BriMA包括一个记忆引导的桥接填补模块，利用任务无关和任务特定的表示来重建缺失的模态，以及一个模态感知的重放机制，根据模态扭曲和分布漂移优先选择信息样本。在三个代表性的多模态AQA数据集（RG、Fis-V和FS1000）上的实验表明，BriMA在不同模态缺失条件下均能提高性能，平均相关性提高6-8%，误差降低12-15%。这些结果展示了在现实部署约束下稳健多模态AQA系统的一小步。
英文摘要：
Action Quality Assessment (AQA) aims to score how well an action is performed and is widely used in sports analysis, rehabilitation assessment, and human skill evaluation. Multi-modal AQA has recently achieved strong progress by leveraging complementary visual and kinematic cues, yet real-world deployments often suffer from non-stationary modality imbalance, where certain modalities become missing or intermittently available due to sensor failures or annotation gaps. Existing continual AQA methods overlook this issue and assume that all modalities remain complete and stable throughout training, which restricts their practicality. To address this challenge, we introduce Bridged Modality Adaptation (BriMA), an innovative approach to multi-modal continual AQA under modality-missing conditions. BriMA consists of a memory-guided bridging imputation module that reconstructs missing modalities using both task-agnostic and task-specific representations, and a modality-aware replay mechanism that prioritizes informative samples based on modality distortion and distribution drift. Experiments on three representative multi-modal AQA datasets (RG, Fis-V, and FS1000) show that BriMA consistently improves performance under different modality-missing conditions, achieving 6--8\% higher correlation and 12--15\% lower error on average. These results demonstrate a step toward robust multi-modal AQA systems under real-world deployment constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
