MMArt-ACM 2022:第五届多媒体艺术作品分析与吸引力计算联合研讨会

Naoko Nitta, Anita Hu, Kensuke Tobitani
{"title":"MMArt-ACM 2022:第五届多媒体艺术作品分析与吸引力计算联合研讨会","authors":"Naoko Nitta, Anita Hu, Kensuke Tobitani","doi":"10.1145/3512527.3531442","DOIUrl":null,"url":null,"abstract":"In addition to classical art types like paintings and sculptures, new types of artworks emerge following the advancement of deep learning, social platforms, media capturing devices, and media processing tools. Large volumes of machine-/user-generated content or professionally-edited content are shared and disseminated on the Web. Novel multimedia artworks, therefore, emerge rapidly in the era of social media and big data. The ever-increasing amount of illustrations/comics/animations on this platform gives rise to challenges of automatic classification, indexing, and retrieval that have been studied widely in other areas but not necessarily for this emerging type of artwork. In addition to objective entities like objects, events, and scenes, studies of cognitive properties emerge. Among various kinds of computational cognitive analyses, we focus on attractiveness analysis in this workshop. The topics of the accepted papers cover the affective analysis of texts, images, and music. The actual MMArt-ACM 2022 Proceedings are available at: https://dl.acm.org/citation.cfm?id=3512730.","PeriodicalId":179895,"journal":{"name":"Proceedings of the 2022 International Conference on Multimedia Retrieval","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MMArt-ACM 2022: 5th Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia\",\"authors\":\"Naoko Nitta, Anita Hu, Kensuke Tobitani\",\"doi\":\"10.1145/3512527.3531442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In addition to classical art types like paintings and sculptures, new types of artworks emerge following the advancement of deep learning, social platforms, media capturing devices, and media processing tools. Large volumes of machine-/user-generated content or professionally-edited content are shared and disseminated on the Web. Novel multimedia artworks, therefore, emerge rapidly in the era of social media and big data. The ever-increasing amount of illustrations/comics/animations on this platform gives rise to challenges of automatic classification, indexing, and retrieval that have been studied widely in other areas but not necessarily for this emerging type of artwork. In addition to objective entities like objects, events, and scenes, studies of cognitive properties emerge. Among various kinds of computational cognitive analyses, we focus on attractiveness analysis in this workshop. The topics of the accepted papers cover the affective analysis of texts, images, and music. The actual MMArt-ACM 2022 Proceedings are available at: https://dl.acm.org/citation.cfm?id=3512730.\",\"PeriodicalId\":179895,\"journal\":{\"name\":\"Proceedings of the 2022 International Conference on Multimedia Retrieval\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2022 International Conference on Multimedia Retrieval\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3512527.3531442\",\"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 2022 International Conference on Multimedia Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3512527.3531442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

除了绘画和雕塑等经典艺术类型之外,随着深度学习、社交平台、媒体捕捉设备和媒体处理工具的发展,新的艺术类型也出现了。大量机器/用户生成的内容或专业编辑的内容在Web上共享和传播。因此,在社交媒体和大数据时代,新的多媒体艺术作品迅速涌现。这个平台上插图/漫画/动画数量的不断增加带来了自动分类、索引和检索的挑战,这些挑战已经在其他领域得到了广泛的研究,但不一定适用于这种新兴的艺术作品类型。除了客体、事件和场景等客观实体之外,还出现了对认知特性的研究。在各种计算认知分析中,本次研讨会主要关注吸引力分析。接受论文的主题包括文本、图像和音乐的情感分析。实际的MMArt-ACM 2022会议记录可在:https://dl.acm.org/citation.cfm?id=3512730上获得。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
MMArt-ACM 2022: 5th Joint Workshop on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia
In addition to classical art types like paintings and sculptures, new types of artworks emerge following the advancement of deep learning, social platforms, media capturing devices, and media processing tools. Large volumes of machine-/user-generated content or professionally-edited content are shared and disseminated on the Web. Novel multimedia artworks, therefore, emerge rapidly in the era of social media and big data. The ever-increasing amount of illustrations/comics/animations on this platform gives rise to challenges of automatic classification, indexing, and retrieval that have been studied widely in other areas but not necessarily for this emerging type of artwork. In addition to objective entities like objects, events, and scenes, studies of cognitive properties emerge. Among various kinds of computational cognitive analyses, we focus on attractiveness analysis in this workshop. The topics of the accepted papers cover the affective analysis of texts, images, and music. The actual MMArt-ACM 2022 Proceedings are available at: https://dl.acm.org/citation.cfm?id=3512730.
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
自引率
0.00%
发文量
0
期刊最新文献
Self-Lifting: A Novel Framework for Unsupervised Voice-Face Association Learning DMPCANet: A Low Dimensional Aggregation Network for Visual Place Recognition Revisiting Performance Measures for Cross-Modal Hashing MFGAN: A Lightweight Fast Multi-task Multi-scale Feature-fusion Model based on GAN Weakly Supervised Fine-grained Recognition based on Combined Learning for Small Data and Coarse Label
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1