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
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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。
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.
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