Affiliation:
1. School of Creativity and Art, Shanghai Tech University, Shanghai 201210, China
2. College of Design and Innovation, Tongji University, Shanghai 200082, China
Abstract
The diversity of big data in Internet of Things is one of the important characteristics that distinguish it from traditional big data. Big data of Internet of Things is often composed of a variety of data with different structural forms. The description of the same thing by these different modal data has certain independence and strong relevance. Accurately and efficiently extracting and processing the hidden fusion information in the big data of the Internet of Things is helpful to solve various multimodal data analysis tasks at present. In this paper, a multimodal interactive function fusion model based on attention mechanism is proposed, which provides more efficient and accurate information for emotion classification tasks. Firstly, a sparse noise reduction self-encoder is used to extract text features, Secondly, features are extracted by encoder. Finally, an interactive fusion module is constructed, which makes text features and image features learn their internal information then the combination function is applied to the emotion classification task.
Subject
Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering
Reference15 articles.
1. Hybrid MAC-based Multipoint Relay with Energy Awareness for System Data Sharing in Wireless Sensor Network
2. Live panoramic surveillance and spatial awareness achieved through optimized array sensor at source data fusion;R. C. Downs;Proceedings of SPIE-The International Society for Optical Engineering,2001
3. A Full-Scale Prototype Multisensor System for Damage Control and Situational Awareness
4. Using heterogeneous multilevel swarms of UAVs and high-level data fusion to support situation management in surveillance scenarios;P. Bouvry
5. Detecting driver distractions using a deep learning approach and multi-source naturalistic driving data Transportation Research Board (TRB);Y. Zhang;99th Annual Meeting,2020
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献