基于Transformer的对比元学习用于低资源可泛化活动识别
Transformer-Based Contrastive Meta-Learning For Low-Resource Generalizable Activity Recognition
AI总结：
本文提出TACO方法，通过Transformer对比元学习合成虚拟目标域并引入监督对比损失，以解决低资源条件下人类活动识别中的分布偏移问题，实验证明其在多种低资源场景下性能显著提升。
摘要：
深度学习已被广泛用于人类活动识别（HAR），但由于分布偏移，将训练好的模型泛化到不同用户和场景仍具挑战性。HAR固有的低资源挑战，即收集和标注足够的人类参与数据可能成本高昂，进一步增加了应对分布偏移的难度。我们提出TACO，一种新颖的基于Transformer的对比元学习方法，用于可泛化的HAR。TACO通过在训练中合成虚拟目标域并显式考虑模型泛化性来应对分布偏移。此外，我们利用Transformer的注意力机制提取富有表现力的特征，并在元优化中引入监督对比损失函数以增强表示学习。我们的评估表明，TACO在各种低资源分布偏移场景下均取得了显著更好的性能。
英文摘要：
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.
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。
