发表机构
Arm Inc.; Northwestern University（Arm公司; 西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模态可穿戴时间序列测试时自适应，提出CARAT方法，通过源训练解耦依赖估计与损坏检测，以轻量级代理指导省略或衰减，在四个数据集上超越现有基线，兼具鲁棒性与计算效率。
AI 中文摘要
多模态可穿戴系统在传感器流变得嘈杂或不可用时必须保持可靠。现有的多模态测试时自适应（TTA）方法通常在线评估可靠性，但当传感器测量不同的物理过程时，跨模态一致性可能具有误导性，且评估替代模态配置会增加推理成本。我们提出CARAT，将模型依赖与运行时损坏检测解耦，以指导省略或衰减，通过源训练摊销依赖估计。一种不对称模态丢弃课程为省略准备了具有缺失韧性的主干，并从窗口化输入投影梯度范数中推导出冻结的、主干特定的依赖代理。在部署时，轻量级单类检测器标记可疑流，代理指导在将可疑集替换为主干训练的缺失符号与在融合前衰减其表示之间进行联合选择，无需候选子集评估。在四个可穿戴数据集、五种损坏类型、三个主干和八个TTA基线上，CARAT实现了最高的整体宏F1和最佳平均排名（2.42），在12个等权数据集-主干设置中超过最强基线EATA 1.58个F1点。在五个配置文件中，CARAT比EATA少使用9.49%的GFLOPs，更新参数少47.82%。跨感知机制也出现了一种模式：多模态TTA方法如PTA在IMU主导的同质数据集上具有竞争力，而单模态TTA方法如TENT和EATA在异质数据集上达到或超过它。这些结果将CARAT定位为可穿戴TTA的实用默认选择，以适度的计算需求提供有竞争力的鲁棒性，其收益因主干和数据集机制而异。
英文摘要
Multimodal wearable systems must remain reliable when sensor streams become noisy or unavailable. Existing multimodal test-time adaptation (TTA) methods often assess reliability online, but cross-modal agreement can be misleading when sensors measure different physical processes, and evaluating alternative modality configurations adds inference cost. We propose CARAT, which decouples model reliance from runtime corruption detection to guide omission or attenuation, amortizing reliance estimation through source training. An asymmetric modality-dropout curriculum prepares a missingness-resilient backbone for omission and derives a frozen, backbone-specific reliance proxy from windowed input-projection gradient norms. At deployment, a lightweight one-class detector flags suspect streams, and the proxy guides a joint choice between replacing the suspect set with the backbone's trained missingness symbol and attenuating its representations before fusion, without candidate-subset evaluation. Across four wearable datasets, five corruption types, three backbones, and eight TTA baselines, CARAT achieves the highest overall macro-F1 and best mean rank (2.42), exceeding EATA, the strongest baseline, by 1.58 F1 points across 12 equally weighted dataset-backbone settings. Across five profiled configurations, CARAT uses 9.49% fewer GFLOPs and updates 47.82% fewer parameters than EATA. A pattern also emerges across sensing regimes: multimodal TTA methods such as PTA are competitive on IMU-dominated homogeneous datasets, whereas unimodal TTA methods like TENT and EATA match or exceed it on heterogeneous datasets. These results position CARAT as a practical default to wearable TTA, offering competitive robustness with modest computational requirements and benefits that vary across backbones and dataset regimes.
