AIART 2021研讨会
发布于 2021-09-27 00:48
AIART 2021
第三届IEEE人工智能与艺术创作国际研讨会
会议介绍
Artificial Intelligence (AI) has already fueled many academic fields as well as industries. In the area of art creation, AI has demonstrated its great potential and gained increasing popularity. People are greatly impressed by AI painting, composing, writing, and designing. AI has not only exhibited a certain degree of creativity, but also helped in uncovering the principles and mechanisms of creativity and imagination from the perspective of neuroscience, cognitive science and psychology.
This is the 3rd AIART workshop to be held on MIPR and it aims to bring forward cutting-edge technologies and most recent advances in the area of AI art in terms of the enabling creation, analysis and understanding technologies. The theme topic of the workshop will be AI creativity. And we plan to organize a Special Issue on a renowned SCI journal. We sincerely invite high-quality papers presenting or addressing issues related to AI art, including but not limited to the following topics:
Theory and practice of AI creativity
Neuroscience, cognitive science and psychology for AI art
AI for painting generation
AI for music/sound synthesis, composing, matching and instrument digital design
AI for poem composing and synthesis
AI for typography and graphic design
AI for fashion, makeup and virtual human
AI for aesthetics understanding, analysis, assessment and prediction
AI for affective computing of artworks
Authentication and copyright issues of AI artworks
此次活动的五个keynote分别是清华大学徐迎庆教授的“风格化的卡通与动画”、中国人民大学金琴教授的“对话中的多模态情感识别”、中国科学技术大学王上飞教授的“非全标注下的面部动作单元识别”、伦敦帝国理工学院Björn W. Schuller教授的“人工创造真实情感：音乐及超越”、伦敦玛丽女王大学Nick Bryan-Kinns教授的“XAIArt：可解释的人工智能与艺术”。另有18篇AIART领域前沿研究论文。
Keynotes (1/5)
Speaker:
徐迎庆教授
Title:
《风格化的卡通与动画》
Time:
2021年9月10日，9:00 – 9:30
Abstract:
摘要:
Computational Aesthetic is the more and more popular topic in recent years.
计算美学是近年来越来越热门的课题。
How to use artificial intelligence to create and generate such as painting, poetry, music, graphics, fashion, etc., is not only a challenge, but also attracts many people's attention.
In this talk, I would like to introduce how to automatically generate cartoon and animation that is part of my previous work at Microsoft Research Asia.
在这次演讲中，我将介绍如何自动生成动画和动画，这是我之前在微软亚洲研究院工作的一部分。
Biography:
人物简介:
Prof. Dr. Ying-Qing Xu is a professor of Academy of Arts & Design, Tsinghua University, Beijing China.
徐英庆教授，清华大学美术学院教授。
At Tsinghua University, he serves as a director of the Future Lab, a director of the Lab for Lifelong Learning, and deputy dean of the Institute for Accessibility Development.
在清华大学，他担任未来实验室主任、终身学习实验室主任、无障碍发展研究所副所长。
His research interesting includes the natural user interface design, immersive perception & interaction, tangible perception & interaction, and e-heritage.
他的研究兴趣包括自然用户界面设计、沉浸式感知与交互、有形感知与交互和电子遗产。
Before joined Tsinghua University, he was a Lead Researcher of Microsoft Research Asia where he had worked for 12 years since January 1999.
在加入清华大学之前，他是微软亚洲研究院的首席研究员，自1999年1月起，他在那里工作了12年。
Dr. Xu has published over 100 peer-reviewed research papers and granted patents.
发表学术论文100余篇，获得多项专利。
He is a fellow of CCF (China Computer Federation), a member of CAA (China Artists Association), and a senior member of IEEE (Institute of Electrical and Electronics Engineers).
他是CCF(中国计算机联合会)会员，CAA(中国美术家协会)会员，IEEE(电子电气工程师学会)高级会员。
Keynotes (2/5)
Speaker:
金琴教授
Title:
《对话中的多模态情感识别》
Time:
2021年9月10日，10:30 – 11:00
Abstract:
文摘:
Understanding human emotions is one of the fundamental steps in establishing natural human-computer interaction systems that possess the emotion perception ability.
理解人类情感是建立具有情感感知能力的自然人机交互系统的基本步骤之一。
Therefore, In the research of intelligent human-computer interaction, the ability of emotion recognition, understanding and expression should also become an indispensable function of an intelligent system.
因此，在智能人机交互的研究中，情感的识别、理解和表达能力也应该成为智能系统不可缺少的功能。
The behavior signals of human emotion expression are multimodal, including voice, facial expression, body language, bio-signals etc.
人类情感表达的行为信号是多模态的，包括声音、面部表情、肢体语言、生物信号等。
In addition, interactive scenario is the natural scene of emotion stimulation and expression, so our research focuses on the integration of multimodal information for emotion perception in interactive scenarios.
此外，交互场景是情感激发和表达的自然场景，因此我们的研究重点是在交互场景中整合多模态信息进行情感感知。
This talk will present our recent works on multimodal emotion recognition in conversations.
这次演讲将介绍我们最近在对话中的多模态情绪识别方面的工作。
Biography:
传记:
Qin Jin is a full professor in School of Information at Renmin University of China (RUC), where she leads the AI·M3 lab.
秦津，中国人民大学信息学院正教授，领导人工智能·M3实验室。
She received her Ph.D. degree in 2007 at Carnegie Mellon University.
她于2007年在卡内基梅隆大学获得博士学位。
Before joining RUC in 2013, she was a research faculty (2007-2012) and a research scientist (2012) at Carnegie Mellon University and IBM China Research Lab.
在2013年加入中国人民大学之前，她曾在卡内基梅隆大学和IBM中国研究实验室担任研究教师(2007-2012)和研究科学家(2012)。
Her research interests are in intelligent multimedia computing and human computer interaction.
主要研究方向为智能多媒体计算和人机交互。
Her team’s recent works on video understanding and multimodal affective analysis have won various awards in international challenge evaluations, including CVPR ActivityNet Dense Video Captioning challenge, NIST TrecVID VTT evaluation, ACM Multimedia Audio=Visual Emotion Challenge etc.
她的团队最近在视频理解和多模态情感分析方面的工作在国际挑战评估中获得了各种奖项，包括CVPR ActivityNet密集视频字幕挑战、NIST TrecVID VTT评估、ACM多媒体音频=视觉情感挑战等。
Keynotes (3/5)
Speaker:
王上飞教授
Title:
《非全标注下的面部动作单元识别》
Time:
2021年9月10日，12:00 – 12:30
Abstract:
文摘:
Facial behavior analysis is one of the fastest growing research areas in affective computing and computer vision.
面部行为分析是情感计算和计算机视觉发展最快的研究领域之一。
We may infer people’s emotions from their facial behavior.
我们可以从人们的面部行为推断出他们的情绪。
There are two commonly used ways to describe facial behavior: facial expression and facial action unit (AU).
描述面部行为有两种常用的方法:面部表情和面部动作单位(AU)。
Facial expression is an intuitive description of facial behavior, most commonly identified as one or more of six expressions (i.e., anger, disgust, fear, happiness, sadness, and surprise).
面部表情是对面部行为的直观描述，通常被认为是六种表情中的一种或多种(例如，愤怒、厌恶、恐惧、快乐、悲伤和惊讶)。
However, the number and definition of expressions are not universally agreed upon by researchers.
然而，表达的数量和定义并没有得到研究者们的一致认可。
AUs are patterns of muscular activation as defined in Ekman’s facial action coding system (FACS).
AUs是埃克曼面部动作编码系统(FACS)中定义的肌肉激活模式。
Compared to expressions, which describe global facial behavior, AUs describe facial behavior in more detail and subtlety.
与描述整体面部行为的表情相比，AUs对面部行为的描述更加详细和微妙。
The successful recognition of AUs could greatly assist the analysis of human facial behavior and expression.
AUs的成功识别将极大地有助于人类面部行为和表情的分析。
Traditional supervised AU detection methods need a large number of AU-annotated facial images.
传统的有监督AU检测方法需要大量的AU标注的人脸图像。
However, AUs should be annotated by experts.
然而，AUs应该由专家注释。
AU labelling is time consuming and expensive.
AU标签既费时又昂贵。
Therefore, we want to reduce reliance on AU labels for AU recognition.
因此，我们希望减少对AU标签的依赖来进行AU识别。
This talk will present our recent works on facial action unit recognition under non-full annotations.
本次演讲将介绍我们在非完整注释下的面部动作单元识别方面的最新工作。
Biography:
传记:
Shangfei Wang received her BS in Electronic Engineering from Anhui University, Hefei, Anhui, China, in 1996.
王尚飞，1996年毕业于中国安徽合肥安徽大学电子工程专业，获学士学位。
She received her MS in circuits and systems, and the PhD in signal and information processing from University of Science and Technology of China (USTC), Hefei, Anhui, China, in 1999 and 2002.
1999年和2002年分别获得安徽合肥中国科学技术大学电路与系统专业硕士学位和信号与信息处理专业博士学位。
From 2004 to 2005, she was a postdoctoral research fellow in Kyushu University, Japan.
2004 - 2005年在日本九州大学从事博士后研究。
Between 2011 and 2012, Dr. Wang was a visiting scholar at Rensselaer Polytechnic Institute in Troy, NY, USA.
2011年至2012年，王博士在美国纽约伦斯勒理工学院(Rensselaer Polytechnic Institute)担任访问学者。
She is currently a Professor of School of Computer Science and Technology, USTC.
现任中国科技大学计算机科学与技术学院教授。
Her research interests cover affective computing and pattern recognition.
主要研究方向为情感计算和模式识别。
She has authored or co-authored over 100 publications.
她撰写或合作发表了100多篇论文。
Keynotes (4/5)
Speaker:
Björn W. Schuller教授
Title:
《人工创造真情实感：音乐及超越》
Time:
2021年9月10日，14:00 – 14:30
Abstract:
“United by emotion” was the slogan of the latest Olympic games, but just as sports, arts are all about emotion. In fact, human perception of art is recently believed to be primarily related to emotional reaction, which is possibly also the main intention behind art’s creation. And contemporary research also laid evidence to experience of art differing from other human “pattern recognition” by integrating the amygdala and likewise emotional activation. In this context, we will discuss AI for art creation in an emotion-conditioned manner. As an example, serves deep learning-based music creation and its conditioning to a target arousal and valence-modelled emotion. In fact, even real-time creation following emotion as recognised in other modalities can be realised in such a way. In this context, we will therefore also discuss automatic recognition of emotion in multimedia. This allows for weakly supervised augmentation of training material or cross-modal emotional conditioning in the artificial emotionally intelligent art creation. Likewise, perhaps oncoming Olympic games could be enriched by online soundtrack generation that picks up the emotional vibe live and supports it by matched epic or dramatic music creation – potentially also fitting the viewer’s personal taste, and other targets.
Biography:
Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany. He is Full Professor of Artificial Intelligence and the Head of GLAM - the Group on Language Audio & Music - at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany, co-founding CEO and current CSO of audEERING – an Audio Intelligence company based near Munich and in Berlin/Germany, Guest Professor at Southeast University in Nanjing/China and permanent Visiting Professor at HIT/China amongst other Professorships and Affiliations. Previous stays include Full Professor at the University of Passau/Germany, and Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France. He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ISCA, President-Emeritus of the AAAC, and Senior Member of the ACM. He (co-)authored 1,000+ publications (35k+ citations, h-index=86), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community. His 30+ awards include having been honoured as one of 40 extraordinary scientists under the age of 40 by the WEF in 2015. He served as Coordinator/PI in 15+ European Projects, is an ERC Starting and DFG Reinhart-Koselleck Grantee, and consultant of companies such as Barclays, GN, Huawei, or Samsung. Outside research, he cherishes arts playing piano and guitars and as a martial artist.
Keynotes (5/5)
Speaker:
Nick Bryan-Kinns教授
Title:
《XAIArt: 可解释的人工智能与艺术》
Time:
2021年9月10日，15:45 – 16:15
Abstract:
文摘:
“United by emotion” was the slogan of the latest Olympic games, but just as sports, arts are all about emotion.
“情同与共”是本届奥运会的口号，但就像体育运动一样，艺术也是情感的结晶。
In fact, human perception of art is recently believed to be primarily related to emotional reaction, which is possibly also the main intention behind art’s creation.
事实上，最近人们认为人类对艺术的感知主要与情感反应有关，这可能也是艺术创作背后的主要意图。
And contemporary research also laid evidence to experience of art differing from other human “pattern recognition” by integrating the amygdala and likewise emotional activation.
当代的研究也通过整合杏仁核和同样的情感激活，为不同于其他人类的“模式识别”艺术体验提供了证据。
In this context, we will discuss AI for art creation in an emotion-conditioned manner.
在此背景下，我们将以情绪性的方式讨论AI在艺术创作中的作用。
As an example, serves deep learning-based music creation and its conditioning to a target arousal and valence-modelled emotion.
例如，基于深度学习的音乐创作及其对目标唤醒和价模式情感的制约。
In fact, even real-time creation following emotion as recognised in other modalities can be realised in such a way.
事实上，即使是在其他模式中被认可的实时情感创造也可以以这种方式实现。
In this context, we will therefore also discuss automatic recognition of emotion in multimedia.
在此背景下，我们也将讨论多媒体中情感的自动识别。
This allows for weakly supervised augmentation of training material or cross-modal emotional conditioning in the artificial emotionally intelligent art creation.
这允许在人工情感智能艺术创作中对训练材料或跨模态情感条件作用进行弱监督增强。
Likewise, perhaps oncoming Olympic games could be enriched by online soundtrack generation that picks up the emotional vibe live and supports it by matched epic or dramatic music creation – potentially also fitting the viewer’s personal taste, and other targets.
同样地，也许即将到来的奥运会也可以通过在线配乐来丰富，在线配乐可以捕捉现场的情感氛围，并通过匹配的史诗或戏剧性的音乐创作来支持它——这可能也符合观众的个人品味和其他目标。
Biography:
传记:
Björn W. Schuller received his diploma, doctoral degree, habilitation, and Adjunct Teaching Professor in Machine Intelligence and Signal Processing all in EE/IT from TUM in Munich/Germany.
Björn W. Schuller毕业于德国慕尼黑工业大学，获工学学士学位、博士学位、康复学学位，并担任机械智能和信号处理副教授。
He is Full Professor of Artificial Intelligence and the Head of GLAM - the Group on Language Audio & Music - at Imperial College London/UK, Full Professor and Chair of Embedded Intelligence for Health Care and Wellbeing at the University of Augsburg/Germany,
他是英国帝国理工学院人工智能的全职教授和GLAM(语言、音频和音乐小组)的负责人，德国奥格斯堡大学医疗保健和福祉嵌入式智能的全职教授和主席。
co-founding CEO and current CSO of audEERING – an Audio Intelligence company based near Munich and in Berlin/Germany, Guest Professor at Southeast University in Nanjing/China and permanent Visiting Professor at HIT/China amongst other Professorships and Affiliations.
audEERING是一家位于德国慕尼黑和柏林的音频智能公司，他是该公司的联合创始人兼首席执行官，现任首席技术官。他是中国南京东南大学的客座教授，中国哈尔滨工业大学的永久客座教授。
Previous stays include Full Professor at the University of Passau/Germany, and Researcher at Joanneum Research in Graz/Austria, and the CNRS-LIMSI in Orsay/France.
之前在德国帕绍大学担任全职教授，在奥地利格拉茨Joanneum研究所担任研究员，在法国奥赛担任法国科学研究中心- limsi研究员。
He is a Fellow of the IEEE and Golden Core Awardee of the IEEE Computer Society, Fellow of the BCS, Fellow of the ISCA, President-Emeritus of the AAAC, and Senior Member of the ACM.
他是IEEE Fellow和IEEE计算机学会黄金核心奖获得者，BCS Fellow, ISCA Fellow, AAAC名誉主席，ACM高级成员。
He (co-)authored 1,000+ publications (35k+ citations, h-index=86), is Field Chief Editor of Frontiers in Digital Health and was Editor in Chief of the IEEE Transactions on Affective Computing amongst manifold further commitments and service to the community.
他(共同)撰写了1000多篇出版物(35k+被引用次数，h-index=86)，是《数字健康前沿》的领域首席编辑，并担任IEEE Transactions on Affective Computing的首席编辑，以及对社区的多种进一步承诺和服务。
His 30+ awards include having been honoured as one of 40 extraordinary scientists under the age of 40 by the WEF in 2015.
他获得的30多个奖项包括2015年世界经济论坛授予的40名40岁以下杰出科学家之一。
He served as Coordinator/PI in 15+ European Projects, is an ERC Starting and DFG Reinhart-Koselleck Grantee, and consultant of companies such as Barclays, GN, Huawei, or Samsung.
他曾担任15+ European Projects的协调员/PI, ERC Starting和DFG Reinhart-Koselleck受让人，以及Barclays、GN、华为或三星等公司的顾问。
Outside research, he cherishes arts playing piano and guitars and as a martial artist.
在研究之外，他热爱钢琴、吉他等艺术，是一名武术家。
会议日程
技术程序委员会
组织者
Luntian Mou
Beijing University of Technology
Beijing, China
ltmou@bjut.edu.cn
Dr. Luntian Mou is an assistant professor with the Faculty of Information Technology, Beijing University of Technology. He was a Visiting Scholar with the University of California, Irvine, from 2019 to 2020. He initiated the international workshop of AIART. His current research interests include personal health navigation, affective computing, intelligent transportation, intelligent multimedia, machine learning and artificial intelligence. He has a research background in multimedia security, copy detection and video fingerprinting. And he serves as a Co-Chair of System subgroup in AVS workgroup and IEEE 1857 workgroup as well. He is a Member of IEEE, ACM, CCF, CSIG, and MPEG.
Feng Gao
Peking University
Beijing, China
gaof@pku.edu.cn
Dr. Feng Gao is an assistant professor with the School of Arts, Peking University. He has long researched in the disciplinary fields of AI and art, especially in AI painting. He co-initiated the international workshop of AIART. Currently, he is also enthusiastic in virtual human. He has demonstrated his AI painting system, called Daozi, in several workshops and drawn much attention.
Zijin Li
Central Conservatory of Music
Beijing, China
lzijin@ccom.edu.cn
Dr. Zijin Li is an associate professor with the Department of AI Music and Music Information Technology, Central Conservatory of Music. She was a Visting Scholar with McGill University. Her current research interests include music acoustics, music creativity, new musical instrument design and Innovation theory of music technology. She is the guest editor of Frontiers: Human-Centred Computer Audition: Sound, Music, and Healthcare and Journal of Cognitive Computation and Systems（JCCS）SI: Perception and Cognition in Music Technology. She is committee chair of New Interface Music Expressions(NIME2021), IEEE MIPR AI Art Workshop , China Sound and Music Technology Conference (CSMT), China AI Music Development Symposium, China Musical Instrument Symposium. She served as the judge of the New Music Device Invention Award of International "Danny award", International Electronic Music Competition (IEMC) and NCDA Awards.
Jiaying Liu
Peking University
Beijing, China
liujiaying@pku.edu.cn
Dr. Jiaying Liu is currently an Associate Professor with the Wangxuan Institute of Computer Technology, Peking University. She received the Ph.D. degree (Hons.) in computer science from Peking University, Beijing China, 2010. She has authored over 100 technical articles in refereed journals and proceedings, and holds 43 granted patents. Her current research interests include multimedia signal processing, compression, and computer vision. Dr. Liu is a Senior Member of IEEE, CSIG and CCF. She was a Visiting Scholar with the University of Southern California, Los Angeles, from 2007 to 2008. She was a Visiting Researcher with the Microsoft Research Asia in 2015 supported by the Star Track Young Faculties Award. She has served as a member of Membership Services Committee in IEEE Signal Processing Society, a member of Multimedia Systems & Applications Technical Committee (MSA TC), Visual Signal Processing and Communications Technical Committee (VSPC TC) in IEEE Circuits and Systems Society, a member of the Image, Video, and Multimedia (IVM) Technical Committee in APSIPA. She received the IEEE ICME 2020 Best Paper Awards and IEEE MMSP 2015 Top10% Paper Awards. She has also served as the Associate Editor of IEEE Trans. on Image Processing, and Elsevier JVCI, the Technical Program Chair of IEEE VCIP-2019/ACM ICMR-2021, the Publicity Chair of IEEE ICME-2020/ICIP-2019, and the Area Chair of CVPR-2021/ECCV-2020/ICCV-2019. She was the APSIPA Distinguished Lecturer (2016-2017).
Wen-Huang Cheng
National Chiao Tung University
Taiwan
whcheng@nctu.edu.tw
Dr. Wen-Huang Cheng is a Professor with the Institute of Electronics, National Chiao Tung University (NCTU), Taiwan, where he is the Founding Director with the Artificial Intelligence and Multimedia Laboratory (AIMMLab). His current research interests include multimedia, artificial intelligence, computer vision, machine learning, social media, and financial technology. He is a co-organizer of the 2018 International Workshop on AI Aesthetics in Art and Media, in conjunction with 2018 ACCV.
Ling Fan
Tezign.com
Tongji University Design Artificial Intelligence Lab
Shanghai, China
lfan@tongji.edu.cn
Dr. Ling Fan is a scholar and entrepreneur to bridge machine intelligence with creativity. He is the founding chair and professor of Tongji University Design Artificial Intelligence Lab. Before, he held teaching position at the University of California at Berkeley and China Central Academy of Fine Arts. Dr. Fan co-founded Tezign.com, a leading technology start-up with the mission to build digital infrastructure for creative contents. Tezign is backed by top VCs like Sequoia Capital and Hearst Ventures. Dr. Fan is a World Economic Forum Young Global Leader, an Aspen Institute China Fellow, and Youth Committee member at the Future Forum. He is also a member of IEEE Global Council for Extended Intelligence.
Dr. Fan received his doctoral degree from Harvard University and master's degree from Princeton University. He recently published From Universality of Computation to the Universality of Imagination, a book on how machine intelligence would influence human creativity.
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