History of AIGC 2023
AIGC 2023 Conference Overview
The 2023 International Conference on AI-generated Content (AIGC 2023) was successfully held on Aug. 25-26, 2023, in Shanghai, China. With support from prestigious institutions like Fudan University, Shanghai AI Laboratory, The Chinese University of Hong Kong, University of Science and Technology of China, RIKEN Center for Advanced Intelligence Project (AIP), The University of Hong Kong, Shanghai Jiao Tong University, Durham University, Nanyang Technological University, AIGC 2023 attracted over 600 participants, fostering vibrant discussions and collaborations. The conference facilitated the exchange of cutting-edge research and insights, shaping the future of AI-generated content. The success of AIGC 2023 underscored its growing significance in the global AI landscape. By bridging academia and industry, the conference facilitated collaborations that pushed the boundaries of AI-generated content. As the field continues to evolve, AIGC remains at the forefront, driving innovation and shaping the future of AI-generated Content.
AIGC 2023 Publication History
Following the conference, the proceedings of AIGC 2023 were published in Springer's Communications in Computer and Information Science (CCIS, volume 1946). This publication, indexed by renowned databases such as EI and Scopus, served as a comprehensive repository of the latest advancements in AI-generated content. By providing a platform for scholars and practitioners to disseminate their findings, AIGC 2023 contributed significantly to the scholarly discourse on artificial intelligence and content creation.
Plenary Speakers
Prof. Licheng Jiao
Xidian University, China
Huashan Distinguished
Professor, the Foreign member of the Academia Europaea, the Foreign member of the
Russian Academy of Natural Sciences
IEEE/IET/CAAI/CAA/CIE/CCF Fellow
Biography: Licheng Jiao, Huashan Distinguished
Professor, the Foreign member of the Academia Europaea, the Foreign member of the
Russian Academy of Natural Sciences, and an IEEE Fellow. He is currently the director of
the Department of Computer Science and Technology of Xidian University, the director of
the Artificial Intelligence Research Institute, the director of the Key Laboratory of
Intelligent Perception and Image Understanding of the Ministry of Education, a member of
the Science and Technology Commission of the Ministry of Education, an expert of the
Ministry of Education’s artificial intelligence technology innovation expert group, and
one of the first batch of Selected into the National Million Talent Project (the first
and second levels), the head of the innovation team of the Changjiang Scholars Program
of the Ministry of Education, the chairman of the “Belt and Road” Artificial
Intelligence Innovation Alliance, the chairman of the Shaanxi Artificial Intelligence
Industry Technology Innovation Strategic Alliance, and the China Artificial Intelligence
The 6th-7th Vice President of the Society, IEEE/IET/CAAI/CAA/CIE/CCF Fellow, has been
selected as Elsevier’s Highly Cited Scholars for seven consecutive years. The main
research directions are intelligent perception and quantum computing, image
understanding and target recognition, deep learning and brain-like computing. He has won
the second prize of the National Natural Science Award, Wu Wenjun Award for Outstanding
Contribution to Artificial Intelligence, Huo Yingdong Young Teacher Award, the title of
National Model Teacher, China Youth Science and Technology Award, and more than ten
provincial and ministerial-level science and technology awards above.
Prof. Xipeng Qiu
Fudan University, China
Biography: Xipeng Qiu is a professor at the School of
Computer Science, Fudan University. He received his B.S. and Ph.D. degrees from Fudan
University. His research interests include natural language processing and deep
learning. He has published more than 60 top journal/conference papers (e.g., TACL, TKDE,
T-ALS, ACL, EMNLP, IJCAI, AAAI, ICCV). He also leads the development of [GitHub] [Google Code] and
FastNLP [GitHub].
Keynote Speakers & Invited Speakers
Prof. Maria Gini
University of Minnesota, USA
ACM/IEEE/AAAI
Fellow
Biography: Maria Gini is a College of Science &
Engineering Distinguished Professor in the Department of Computer Science and
Engineering at the University of Minnesota. She works on decentralized decision-making
for autonomous agents in many application domains, ranging from swarm robotics to task
allocation, methods to explore unknown environments, and navigation in dense crowds. She
is a Fellow of AAAI, ACM, and IEEE and won numerous awards for teaching, mentoring,
increasing participation of women and underrepresented members in computing, and for
service. She has published extensively in journals, conferences, and books. She is
Editor in Chief of Robotics and Autonomous Systems and is on the editorial board of
other journals, including Autonomous Agents and Multi-Agent Systems.
Speech Title: A call to arms to AI researchers: combat deep fakes
Abstract: With the rapid diffusion of large language models, new
opportunities have opened up but also new challenges. It is up to AI researchers to find
ways to prevent problems caused by those models, such as privacy violation,
discrimination, lack of security, and deep fakes. Some problems require the introduction
of regulations on AI systems, but some, like deep fakes, require also technical
solutions, which call for engagement of AI researchers.
Prof. SIAU Keng Leng
City University of Hong Kong, Hong Kong SAR, China
AIS Fellow
Biography: Professor Siau is the Head of the Department
of Information Systems and Chair Professor of Information Systems at the City University
of Hong Kong. He is also an Affiliate Chair Professor of the School of Data Science at
the City University of Hong Kong. Before joining CityU, he was the Head (Dean
equivalent) of Business Programs at the Missouri University of Science and Technology
(formerly the University of Missouri-Rolla). Professor Siau received his Ph.D. in
Business Administration with a specialization in Management Information Systems from the
University of British Columbia. His M.Sc. and B.Sc. (honors) degrees are in Computer and
Information Sciences from the National University of Singapore. Professor Siau has more
than 300 academic publications. According to Google Scholar, he has a citation count of
more than 20,000. His h-index and i10-index, according to Google Scholar, are 72 and
186, respectively. Professor Siau is consistently ranked among the top information
systems researchers worldwide based on his h-index and productivity rate. He is listed
in 2020, 2021, and 2022 as one of the world's top 2% most-cited scientists (i.e., ranked
in the top 1%) by Stanford University's studies. He has been involved in externally
funded projects totaling over US$6 million by NSF, IBM, NSFC, and other business
organizations. He was a recipient of the prestigious International Federation for
Information Processing (IFIP) Outstanding Service Award in 2006, IBM Faculty Awards in
2006 and 2008, IBM Faculty Innovation Award in 2010, AIS Sandra Slaughter Service Award
in 2019, AIS Award for Outstanding Contribution to IS Education in 2019, and AIS Fellow
Award in 2022.
Prof. Deyi Xiong
Tianjin University, China
Biography: Deyi Xiong is a Professor of Computer Science
at Tianjin University (TJU), Director of both the Natural Language Processing Laboratory
at the College of Intelligence and Computing, TJU and the International Joint Research
Center of Language Intelligence and Technology at TJU. Prior to joining TJU, he was a
professor at Soochow University (2013-2018) and a research scientist at the Institute
for Infocomm Research, Singapore (2007-2013).
His research focuses on natural
language processing, specifically machine translation, dialogue, natural language
generation and question answering. He has published over 100 papers in prestigious
journals and conferences, including Computational Linguistics, IEEE TPAMI, IEEE TASLP,
Artificial Intelligence, AAAI, IJCAI ACL, and EMNLP. He is the first author of the book
Linguistically Motivated Statistical Machine Translation: Models and Algorithms
published by Springer and the Chinese book Neural Machine Translation: Foundations,
Principles, Practices and Frontiers.
He was the program co-chair of IALP 2021 and
CWMT 2017. He has also served as an area chair of conferences including ACL, EMNLP,
NAACL and COLING. He was the founder and co-organizer of multiple
ACL/EMNLP/NAACL-affiliated workshops such as S2MT 2015, SedMT 2016 and DiscoMT 2019. He
is a member of the standing committee of reviewers of CL, action editor of both TACL and
ARR, and an editorial board member of International Journal of Asian Language
Processing. He was a council member of the Chinese and Oriental Languages Information
Processing Society (COLIPS), Singapore and Honorary Treasurer of the Asian Federation of
Natural Language Processing (AFNLP). He has been a council member of the Chinese
Information Processing Society of China (CIPSC) since 2016.
演讲题目：大语言模型对齐及趋势
摘要：作为AIGC重要基座的大语言模型，近年来发展迅猛。其能力的不断突破，使得人们对大语言模型存在的社会伦理风险及其对人类生存构成的潜在威胁产生了普遍的担忧。本报告将介绍与大语言模型及通用智能体安全密切相关的对齐技术，简述其存在的社会和技术挑战，分析大语言模型对齐的主要技术路线和方法，探讨如何对大语言模型对齐进行评测，并对未来趋势进行展望。
Prof. Xiuxian Li
Tongji University, China
Biography: Xiuxian Li is a professor of College of
Electronic and Information Engineering, and Shanghai Research Institute for Intelligent
Autonomous Systems, Tongji University, Shanghai, China. He received the B.S. degree in
mathematics and applied mathematics and the M.S. degree in pure mathematics in Shandong
University, Jinan, China, in 2009 and 2012 respectively, and the Ph.D. degree in
mechanical engineering from the University of Hong Kong in 2016. From 2016 to 2020, he
has been a research fellow in the School of Electrical and Electronic Engineering,
Nanyang Technological University, Singapore, and he has also been a senior research
associate in the Department of Biomedical Engineering, CityU, Hong Kong in 2018. He is
in the finalist of IEEE RCAR 2018, members of AAAI, CAA-YAC, CAA, CAAI, and senior
members of IEEE, CICC. He has published more than 60 papers. His research interests
include distributed control and optimization, game theory, machine learning, and
autonomous vehicles (e.g., UAVs).
Speech Title: Decentralized Online Optimization and Online Games
Abstract: Online learning is a popular paradigm for decision making in
dynamic even adversarial environments. With the advent of big data and advanced
technologies, etc., decentralized online learning has thus far been increasingly focused
in the last decade, where a group of agents commit their decisions via local
communication in dynamic environments, which is usually classified into decentralized
online optimization (DOO) and online games (OG) according to cooperative and
noncooperative characteristics among all the agents, respectively. This talk aims to
briefly introduce decentralized online learning and present some cutting-edge
developments in three aspects, i.e., DOO with coupled inequality constraints,
decentralized online aggregative optimization, and online game with time-varying coupled
inequality constraints. For each scenario, decentralized online algorithms are proposed
with guaranteed performances, i.e., sublinear static/dynamic regret.
Prof. Jianke Zhu
Zhejiang University, China
Biography: Jianke Zhu is a Professor in College of
Computer Science at Zhejiang University. He received his Ph.D degree in Computer Science
and Engineering from The Chinese University of Hong Kong. He was a postdoc in BIWI
Computer Vision Lab at ETH Zurich. Dr. Zhu's research interests include computer vision
and machine learning. He is a senior member of the IEEE.
Speech Title: Human-centric AIGC: Neural Reconstruction and
Rendering
演讲题目：以人为本的AIGC：神经重建与渲染
Prof. Wanyun Cui
Shanghai University of Finance and Economics, China
Biography: Wanyun Cui is an associate professor at the
School of Information Management and Engineering, Shanghai University of Finance and
Economics. He received his PhD from Fudan University in 2017, advised by Wei Wang and
Yanghua Xiao. His research interests lie at the intersection of language and knowledge.
Now he is particularly interested in large language models (LLMs). He has published
several papers as first author in NeurIPS, ICLR, SIGMOD, PVLDB, IJCAI, AAAI, ACL, and
EMNLP. He has been recognized as an AI 2000 Most Influential Scholar (2012-2022) and has
won the ACM China Outstanding Doctoral Dissertation Nomination Award (top 4 in China)
and the ACM Shanghai Outstanding Doctoral Dissertation Award (top 2 in Shanghai).
演讲题目：如何利用大语言模型优化金融决策
Dr. Shushen Lu
Founder and Chief Researcher of Shensi Academy
个人简介: 陆树燊，慎思学社创始人&首席研究员，微信创始团队成员，产品创新专家。他曾深度参与微信客户端、微信支付、开放平台和公众平台的建设，也曾多次创业，为故宫、颐和园、永旺、复星、京东等著名机构提供数字化、产品创新方面的业务支持和指导。他同时也是36氪、虎嗅网专栏作家，多篇商业评论和产品案例文章在行业内广泛传播。他目前主要研究AI技术的产品创新，为企业提供AI产品诊断和辅导。慎思学社是行业领先的AI产品智库，中小型创新企业的“共享研究院”。目前重点关注AIGC与各垂直行业的落地实施、产品创新等领域。
演讲题目: 拥抱场景，迎接AI应用潮
Dr. Liefeng Bo
Head of XR Lab at Alibaba
Group
Biography: Liefeng Bo (Member, IEEE) received the Ph.D.
degree from Xidian University, in 2007. He is currently Head of XR Lab at Alibaba Group.
He was a Principal Scientist at Amazon, where he led a research team at Amazon Go for
turning innovative research into products. He was a Postdoctoral Researcher successively
at the Toyota Technological Institute at the University of Chicago and the University of
Washington. His research interests are in machine learning, deep learning, computer
vision, robotics, and natural language processing. He published more than 60 papers in
top conferences and journals with more than 7000 Google Scholar citations. He won the
National Excellent Doctoral Dissertation Award of China, in 2010, and the Best Vision
Paper Award in ICRA 2011.
薄列峰博士2022年8月加入阿里巴巴，担任达摩院XR实验室负责人，致力于研究数字人、3D内容生成等前沿技术。他于2007年获西安电子科技大学博士学位，2007-2012年期间先后在芝加哥大学丰田研究院和华盛顿大学从事博士后研究。薄博士于2013年8月加入亚马逊，担任Principal
Applied Scientist，负责Amazon
Go无人零售店AI算法的研发与落地。他于2017年10月加入京东集团，担任京东科技AI实验室首席科学家，负责前沿人工智能技术的研发与落地。
薄博士在Neurips、CVPR、ICCV、ICML、AAAI、ICRA、IJCV等国际顶级会议和期刊共计发表论文100余篇，论文被引用超12000次，H指数52，其中博士论文荣获全国百篇优秀博士论文奖，RGB-D物体识别论文荣获机器人顶级会议ICRA最佳计算机视觉论文奖，担任过多个顶级人工智能会议程序委员会委员。
演讲题目：数字人与3D内容生成
Invited Speakers (Alphabetize by Last Name)
Dr. Hyunwoo Kim
Senior Research Expert, Zhejiang
Lab
Biography: Hyunwoo Kim is a Senior Research Expert and a
Principal Investigator at Zhejiang Lab, Hangzhou, China. Prior to that, he worked at LG
AI Research, LG Electronics, Kakao Corp., and Samsung Electronics, Seoul, Korea, as
senior-level researchers and technical leaders, Also, he worked at the Beijing Institute
of Technology, Beijing, China, and the Korean German Institute of Technology, Seoul,
Korea, as an associate professor and an assistant professor, respectively. He received
his Ph.D. degree from the School of Electrical and Computer Engineering at POSTECH,
Pohang, Korea.
He has been long engaged in computer vision and deep learning and
published more than 50 international journals / proceedings and US patents with a
citation count over 2,100. He is the recipients of the first prize in the CVPR 2020
CLVision Challenge, 2020 LG Awards presented by LG Corp., and 2006 iF Design Awards in
Germany. In addition, his face image retrieval is an international standard of MPEG-7
ISO/IEC and the large-scale image retrieval method has been successfully commercialized.
His current research interest includes generative modeling, representation learning,
video analysis and generation, and 3D vision and graphics.
Speech title: A Tale of Two Deep Models: Generative modeling and
Representation Learning
Dr. Luziwei Leng
Principal Engineer at Huawei ACS
Lab
Biography: Dr. Luziwei Leng received his Bachelor degree
from UESTC and Doctoral degree from the Department of Physics, Heidelberg University,
Germany in 2019. He is currently a Principal Engineer at Huawei ACS Lab and a subproject
leader of the Science and Technology Innovation 2030-Major Project: Brain Science and
Brain-Like Intelligence Technology：Brain-Inspired Chip Architecture for Online Learning.
His research focuses on brain-inspired computing, including neuro-inspired bottom-up
directions such as spiking neural networks, local learning algorithms and
functional-driven top-down directions including predictive coding, self-supervised
learning and continuous learning. He is also interested in neuromorphic hardware design
and diverse applications of brain-inspired algorithms in computer vision, neural
population decoding, large models and robotics. As leading author, he has published
several top conference papers on CVPR and NeurIPS. He also serves as a reviewer for
CVPR, ICCV, ECCV and NeurIPS.
Speech Title: Towards low-power, efficient AIGC - a perspective from
brain-inspired computing
Dr. Xiaogang Wang
Senior Researcher in LIGHTSPEED
STUDIOS, Singapore
Biography: Xiaogang Wang is a senior researcher in
LIGHTSPEED STUDIOS, Singapore, where he focuses on applying computer vision techniques
to design and generate 3D scenes. Before that, he was a senior engineer at Motional,
Singapore, where he conducts BEV perceptions and HD map generation. Before joining
Motional, he was a research fellow at National University of Singapore (NUS) from Oct.
2020 to Sep. 2021. Prior to that, he did his PhD in NUS from Aug. 2016 to Aug. 2020. He
has published several top journals and conference papers, including IEEE TPAMI, CVPR,
ICCV, etc. His research focuses on deep learning and computer vision, especially for 3D
generation and reconstruction. He also serves as a reviewer for TPAMI, TNNLS, TVCG, TIP,
RA-L, ICCV, ECCV, CVPR, IROS and ICRA.
Dr. Nayyar Zaidi
Deakin University,
Australia
Biography: Dr. Zaidi is a member of Centre for Cyber
Resilience and Trust (CREST) at Deakin University and leads AI activities of the group.
He works as Sr. Lecturer (eq Associate Professor in China) at Deakin University,
Burwood, Australia. Dr. Zaidi is a distinguished researcher in Machine Learning and
Artificial Intelligence, and has a track record of publishing in top journals such as
‘Journal of Machine Learning Research’, ‘Springer Machine Learning’, ‘Springer DMDK’,
etc. His pure research constitutes topics in `data generation’, `robust machine
learning’, `large-scale machine learning’, and `effective feature engineering’. His
applied research is focused on `network security anomaly detection’, `human dialogue
evaluation, `financial risk management’, etc. He has published over 40 peer-reviewed
articles and serves as program committee member for major conferences such as IEEE ICDM,
KDD, IJCAI, SDM, PAKDD and many others.
Speech Title: Deep Generative Models are great for Structured Data --
well, what about Tabular Data?