@inproceedings{c03fad0dae054ff1a42de72d66cc914a,
title = "AdaFlow: Non-blocking Inference with Heterogeneous Multi-modal Mobile Sensor Data",
abstract = "Multi-modal deep learning offer conceptual advantages by integrating information from different vantage points. However, data flow blocking and corruption due to modalities asymmetric which make exist methods can hardly balance latency and accuracy still pose many challenges. To address these challenges, this paper proposes AdaFlow to establish a multi-modal mapping mechanism using an affinity matrix, achieving non-blocking data flow in multi-modal systems.",
keywords = "Affinity Matrix, Multi-modal, Non-blocking Data Flow",
author = "Fengmin Wu and Sicong Liu and Bin Guo and Xiaocheng Li and Yuan Gao and Zhiwen Yu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024 ; Conference date: 13-05-2024",
year = "2024",
doi = "10.1109/CSCAIoT62585.2024.00006",
language = "英语",
series = "Proceedings - 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "8--9",
booktitle = "Proceedings - 2024 IEEE Coupling of Sensing and Computing in AIoT Systems, CSCAIoT 2024",
}