针对特定场景优化动作识别模型，智能筛选有用的动作先验。
From General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition
- 通过迭代剪枝与优化，动态筛选任务相关的动作模式。
- 在阿尔茨海默病和跌倒检测任务中性能与效率均优于基线。
- 适合医疗监测等小范围、高精度需求的应用场景。
基于骨架的动作识别模型在大规模通用动作数据集上表现优异，但直接迁移到医疗监测等特定领域时效果往往不理想，因为这些任务范围狭窄，仅涉及部分通用运动先验。且并非所有预训练运动模式都对特定任务有用，保留无关组件会阻碍适应并增加计算开销。为此，本文提出骨架先验自适应迁移框架PATS，通过选择性保留任务相关运动先验、过滤冗余成分来优化模型迁移。PATS沿用标准流程：从视频中提取骨架信号，使用在通用动作上预训练的时空骨干网络。核心是自适应先验迁移模块，通过迭代剪枝与精炼实现模型压缩与先验选择。在阿尔茨海默病检测与跌倒检测两个具体任务上的实验表明，PATS在性能与效率上均显著优于现有基线方法。代码将在论文录用后公开。
原文摘要 · Abstract (English)
Skeleton-based action recognition models have recently shown strong performance on large-scale benchmarks with general actions. However, directly transferring them to domain-specific tasks e.g., healthcare monitoring, is often suboptimal, as such tasks are narrow in scope and may be relevant to only a subset of general motion priors. Moreover, not all pretrained motion patterns are equally useful for a specific task, and retaining less relevant components may hinder adaptation and increase computational cost. To address these challenges, we propose Prior-Adaptive Transfer of Skeletons (PATS), a framework that adapts general skeleton-based models by selectively retaining task-relevant motion priors while filtering redundant ones during transfer. PATS follows a standard pipeline that extracts skeleton signals from videos and employs a spatio-temporal backbone pre-trained on general actions. The key contribution lies in a novel Adaptive Prior Transfer module, which performs model compression as a prior selection mechanism through iterative pruning and refinement. Experiments on two specific action recognition tasks, Alzheimer's detection and fall detection, show consistent improvements in both performance and efficiency over competitive baselines. The code will be released upon acceptance.
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