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}
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.