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• W4306250089 abstract "Human activity recognition (HAR) is a key enabling technology for a wide range of emerging applications. Although multimodal sensing systems are essential for capturing complex and dynamic human activities in real-world settings, they bring several new challenges including limited labeled multimodal data. In this paper, we propose Cosmo, a new system for contrastive fusion learning with small data in multimodal HAR applications. Cosmo features a novel two-stage training strategy that leverages both unlabeled data on the cloud and limited labeled data on the edge. By integrating novel fusion-based contrastive learning and quality-guided attention mechanisms, Cosmo can effectively extract both consistent and complementary information across different modalities for efficient fusion. Our evaluation on a cloud-edge testbed using two public datasets and a new multimodal HAR dataset shows that Cosmo delivers significant improvement over state-of-the-art baselines in both recognition accuracy and convergence delay." @default.
• W4306250089 created "2022-10-15" @default.
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• W4306250089 date "2022-10-14" @default.
• W4306250089 modified "2023-10-05" @default.
• W4306250089 title "Cosmo" @default.
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• W4306250089 doi "https://doi.org/10.1145/3495243.3560519" @default.
• W4306250089 hasPublicationYear "2022" @default.
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