{"title":"学习带有情绪的活动模式","authors":"Qi Wang, T. Artières, Yu Ding","doi":"10.1145/2948910.2948958","DOIUrl":null,"url":null,"abstract":"This paper is a preliminary work towards the design of a model able to generate realistic motion sequences conditioned on a number of contextual variables like age, morphology, emotion etc. We focus in a first step on the design of contextual markovian models able to perform recognition of activities performed under various emotions even in the case no training samples are available for a particular (activity, emotion) pair, a zero shot learning setting.","PeriodicalId":381334,"journal":{"name":"Proceedings of the 3rd International Symposium on Movement and Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Learning Activity Patterns Performed With Emotion\",\"authors\":\"Qi Wang, T. Artières, Yu Ding\",\"doi\":\"10.1145/2948910.2948958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper is a preliminary work towards the design of a model able to generate realistic motion sequences conditioned on a number of contextual variables like age, morphology, emotion etc. We focus in a first step on the design of contextual markovian models able to perform recognition of activities performed under various emotions even in the case no training samples are available for a particular (activity, emotion) pair, a zero shot learning setting.\",\"PeriodicalId\":381334,\"journal\":{\"name\":\"Proceedings of the 3rd International Symposium on Movement and Computing\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Symposium on Movement and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2948910.2948958\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Symposium on Movement and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2948910.2948958","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper is a preliminary work towards the design of a model able to generate realistic motion sequences conditioned on a number of contextual variables like age, morphology, emotion etc. We focus in a first step on the design of contextual markovian models able to perform recognition of activities performed under various emotions even in the case no training samples are available for a particular (activity, emotion) pair, a zero shot learning setting.
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