Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hindered by the limited availability of sensor data and resource constraints of edge systems. While transferring pre-trained models to different sensing applications is promising, existing methods often require extensive sensor data and computational resources, resulting in high costs and limited transferability. In this paper, we propose XTransfer, a first-of-its-kind method enabling modality-agnostic, few-shot model transfer with resource-efficient design. XTransfer flexibly uses pre-trained models and transfers knowledge across different modalities by (i) model repairing that safely mitigates modality shift by adapting pre-trained layers with only few sensor data, and (ii) layer recombining that efficiently searches and recombines layers of interest from source models in a layer-wise manner to restructure models. We benchmark various baselines across diverse human sensing datasets spanning different modalities. The results show that XTransfer achieves state-of-the-art performance while significantly reducing the costs of sensor data collection, model training, and edge deployment.

翻译：边缘系统中基于深度学习的人体感知技术为智能应用带来了巨大潜力。然而，传感器数据的有限性以及边缘系统的资源约束阻碍了其训练与开发。尽管将预训练模型迁移至不同感知应用具有前景，但现有方法通常需要大量传感器数据与计算资源，导致成本高昂且可迁移性受限。本文提出XTransfer，这是一种首创的、支持模态无关小样本模型迁移且具备资源高效设计的方法。XTransfer通过以下方式灵活利用预训练模型并实现跨模态知识迁移：(i) 模型修复：仅使用少量传感器数据调整预训练层，以安全缓解模态偏移；(ii) 层重组：以逐层方式高效搜索并重组源模型中感兴趣的层，以重构模型。我们在涵盖不同模态的多种人体感知数据集上对各类基线方法进行了基准测试。结果表明，XTransfer在显著降低传感器数据采集、模型训练及边缘部署成本的同时，实现了最先进的性能。