Egocentric video-based models capture rich semantic information and have demonstrated strong performance in human activity recognition (HAR). However, their high power consumption, privacy concerns, and dependence on lighting conditions limit their feasibility for continuous on-device recognition. In contrast, inertial measurement unit (IMU) sensors offer an energy-efficient and privacy-preserving alternative, yet they suffer from limited large-scale annotated datasets, leading to weaker generalization in downstream tasks. To bridge this gap, we propose COMODO, a cross-modal self-supervised distillation framework that transfers rich semantic knowledge from the video modality to the IMU modality without requiring labeled annotations. COMODO leverages a pretrained and frozen video encoder to construct a dynamic instance queue, aligning the feature distributions of video and IMU embeddings. By distilling knowledge from video representations, our approach enables the IMU encoder to inherit rich semantic information from video while preserving its efficiency for real-world applications. Experiments on multiple egocentric HAR datasets demonstrate that COMODO consistently improves downstream classification performance, achieving results comparable to or exceeding fully supervised fine-tuned models. Moreover, COMODO exhibits strong cross-dataset generalization. Benefiting from its simplicity, our method is also generally applicable to various video and time-series pre-trained models, offering the potential to leverage more powerful teacher and student foundation models in future research. The code is available at https://github.com/Breezelled/COMODO .

翻译：基于第一人称视角的视频模型能够捕获丰富的语义信息，并在人类活动识别任务中展现出优异的性能。然而，其高功耗、隐私顾虑以及对光照条件的依赖，限制了其在设备端持续识别应用中的可行性。相比之下，惯性测量单元传感器提供了一种高能效且保护隐私的替代方案，但其面临大规模标注数据集有限的问题，导致在下游任务中泛化能力较弱。为弥合这一差距，我们提出了COMODO，一种跨模态自监督蒸馏框架，该框架能够在无需标注的情况下，将丰富的语义知识从视频模态迁移至IMU模态。COMODO利用预训练且冻结的视频编码器构建动态实例队列，对齐视频与IMU嵌入的特征分布。通过从视频表征中蒸馏知识，我们的方法使IMU编码器能够继承视频的丰富语义信息，同时保持其在现实应用中的高效性。在多个第一人称人类活动识别数据集上的实验表明，COMODO持续提升了下游分类性能，取得了与全监督微调模型相当甚至更优的结果。此外，COMODO展现出强大的跨数据集泛化能力。得益于其简洁性，本方法还可广泛适用于各类视频与时间序列预训练模型，为未来研究中利用更强大的教师与学生基础模型提供了潜力。代码已发布于 https://github.com/Breezelled/COMODO。