用Transformer和对比学习提升低资源下的活动识别泛化能力
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition
- 基于Transformer的对比元学习，合成虚拟目标域增强泛化
- 在多种低资源场景下性能显著优于现有方法
- 适合数据稀缺但需跨用户跨场景应用的活动识别任务
深度学习已广泛应用于人体活动识别（HAR），但模型在不同用户和场景间的泛化仍面临分布偏移（DS）挑战。由于收集和标注人体相关数据成本高昂，HAR固有的低资源问题进一步加剧了应对分布偏移的难度。本文提出TACO——一种新型的基于Transformer的对比元学习方法，用于可泛化的HAR。TACO通过在训练中显式考虑模型泛化性，合成虚拟目标域以缓解分布偏移。同时，利用Transformer的注意力机制提取丰富特征，并在元优化中引入监督对比损失函数，增强表征学习能力。实验表明，TACO在多种低资源分布偏移场景下均表现出显著更优的性能。
原文摘要 · Abstract (English)
Deep learning has been widely adopted for human activity recognition (HAR) while generalizing a trained model across diverse users and scenarios remains challenging due to distribution shifts. The inherent low-resource challenge in HAR, i.e., collecting and labeling adequate human-involved data can be prohibitively costly, further raising the difficulty of tackling DS. We propose TACO, a novel transformer-based contrastive meta-learning approach for generalizable HAR. TACO addresses DS by synthesizing virtual target domains in training with explicit consideration of model generalizability. Additionally, we extract expressive feature with the attention mechanism of Transformer and incorporate the supervised contrastive loss function within our meta-optimization to enhance representation learning. Our evaluation demonstrates that TACO achieves notably better performance across various low-resource DS scenarios.
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