具有深度智能的大型跨模式社交媒体数据分析

IEEE Multim. Pub Date : 2020-10-01 DOI:10.1109/mmul.2020.3034589

Yang Wang, Meng Fang, Joey Tianyi Zhou, Tingting Mu, D. Tao

{"title":"具有深度智能的大型跨模式社交媒体数据分析","authors":"Yang Wang, Meng Fang, Joey Tianyi Zhou, Tingting Mu, D. Tao","doi":"10.1109/mmul.2020.3034589","DOIUrl":null,"url":null,"abstract":"The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security.","PeriodicalId":290893,"journal":{"name":"IEEE Multim.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Big Cross-Modal Social Media Data Analytics With Deep Intelligence\",\"authors\":\"Yang Wang, Meng Fang, Joey Tianyi Zhou, Tingting Mu, D. Tao\",\"doi\":\"10.1109/mmul.2020.3034589\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security.\",\"PeriodicalId\":290893,\"journal\":{\"name\":\"IEEE Multim.\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Multim.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/mmul.2020.3034589\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Multim.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/mmul.2020.3034589","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

本专题的13篇论文聚焦于深度智能的社交媒体数据分析。基于深度智能的大跨模型社交媒体数据分析旨在处理来自多模态深度空间的数据采样，从而更好地表征大数据。讨论的主题范围从人类行为识别到情感计算、灾难检测、分类、检索、聚类、车辆重新识别和数据安全。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Big Cross-Modal Social Media Data Analytics With Deep Intelligence

The thirteen papers in this special section focus on social media data analytics with deep intelligence. Big cross-model social media data analytics with deep intelligence aims to handle data sampling from multimodal deep spaces, so as to well characterize the big data. The addressed topic span from the range of human action recognition to affective computing, disaster detection, classification, retrieval, clustering, vehicle reidentification, and data security.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

IEEE Multim.

自引率

0.00%

发文量