解决人体动作识别中因个体差异导致模型泛化差的问题。
Subject Invariant Contrastive Learning for Human Activity Recognition
- 通过重加权同人负样本，抑制个体特征干扰。
- 在三个数据集上提升性能最高达11%。
- 适配多种自监督与监督学习场景，通用性强。
标注数据成本高，使得自监督方法（如对比学习）在人体动作识别（HAR）中备受关注。有效的对比学习依赖于选择有信息量的正负样本，但传感器信号受个体差异影响大，产生显著领域偏移，导致模型将个体特异性特征而非动作特异性特征编码进嵌入空间，从而降低对未见个体的泛化能力。为此，本文提出主体无关对比学习（SICL），一种简单但有效的损失函数，通过重加权来自同一主体的负样本，抑制个体特异性线索，强化动作特异性信息。我们在UTD-MHAD、MMAct和DARai三个公开基准上评估该方法，结果表明SICL相比传统对比学习可提升性能最高达11%。此外，我们还展示了该损失函数在多种设置下的适应性，包括不同自监督方法、多模态场景及监督学习框架。
原文摘要 · Abstract (English)
The high cost of annotating data makes self-supervised approaches, such as contrastive learning methods, appealing for Human Activity Recognition (HAR). Effective contrastive learning relies on selecting informative positive and negative samples. However, HAR sensor signals are subject to significant domain shifts caused by subject variability. These domain shifts hinder model generalization to unseen subjects by embedding subject-specific variations rather than activity-specific features. As a result, human activity recognition models trained with contrastive learning often struggle to generalize to new subjects. We introduce Subject-Invariant Contrastive Learning (SICL), a simple yet effective loss function to improve generalization in human activity recognition. SICL re-weights negative pairs drawn from the same subject to suppress subject-specific cues and emphasize activity-specific information. We evaluate our loss function on three public benchmarks: UTD-MHAD, MMAct, and DARai. We show that SICL improves performance by up to 11% over traditional contrastive learning methods. Additionally, we demonstrate the adaptability of our loss function across various settings, including multiple self-supervised methods, multimodal scenarios, and supervised learning frameworks.
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