【ICIG2021】Latest News & Announcements of the Plenary Talk1

2021 年 11 月 1 日 中国图象图形学学会CSIG

The 11th International Conference on Image and Graphics (ICIG) will be held in Haikou, China, on November 26 – 28, 2021. We sincerely invite the researches over the world in this area to join us.


Speakers




Prof. Matthias Nießner
Technical University of Munich
Talk Title:  The Revolution of Neural Rendering
Abstract: In this talk, I will present our research vision in how to create a photo-realistic digital replica of the real world, and how to make holograms become a reality. Eventually, I would like to see photos and videos evolve to become interactive, holographic content indistinguishable from the real world. Imagine taking such 3D photos to share with friends, family, or social media; the ability to fully record historical moments for future generations; or to provide content for upcoming augmented and virtual reality applications. AI-based approaches, such as generative neural networks, are becoming more and more popular in this context since they have the potential to transform existing image synthesis pipelines. I will specifically talk about an avenue towards neural rendering where we can retain the full control of a traditional graphics pipeline but at the same time exploit modern capabilities of deep learning, such as handling the imperfections of content from commodity 3D scans. While the capture and photo-realistic synthesis of imagery open up unbelievable possibilities for applications ranging from entertainment to communication industries, there are also important ethical considerations that must be kept in mind. Specifically, in the content of fabricated news (e.g., fake-news), it is critical to highlight and understand digitally-manipulated content. I believe that media forensics plays an important role in this area, both from an academic standpoint to better understand image and video manipulation, but even more importantly from a societal standpoint to create and raise awareness around the possibilities and moreover, to highlight potential avenues and solutions regarding trust of digital content.
Biography: Dr. Matthias Nießner is a Professor at the Technical University of Munich where he leads the Visual Computing Lab. Before, he was a Visiting Assistant Professor at Stanford University. Prof. Nießner’s research lies at the intersection of computer vision, graphics, and machine learning, where he is particularly interested in cutting-edge techniques for 3D reconstruction, semantic 3D scene understanding, video editing, and AI-driven video synthesis. In total, he has published over 70 academic publications, including 22 papers at the prestigious ACM Transactions on Graphics (SIGGRAPH / SIGGRAPH Asia) journal and 43 works at the leading vision conferences (CVPR, ECCV, ICCV); several of these works won best paper awards, including at SIGCHI’14, HPG’15, SPG’18, and the SIGGRAPH’16 Emerging Technologies Award for the best Live Demo. Prof. Nießner’s work enjoys wide media coverage, with many articles featured in main-stream media including the New York Times, Wall Street Journal, Spiegel, MIT Technological Review, and many more, and his was work led to several TV appearances such as on Jimmy Kimmel Live, where Prof. Nießner demonstrated the popular Face2Face technique; Prof. Nießner’s academic Youtube channel currently has over 5 million views. For his work, Prof. Nießner received several awards: he is a TUM-IAS Rudolph Moessbauer Fellow (2017 – ongoing), he won the Google Faculty Award for Machine Perception (2017), the Nvidia Professor Partnership Award (2018), as well as the prestigious ERC Starting Grant 2018 which comes with 1.500.000 Euro in research funding; in 2019, he received the Eurographics Young Researcher Award honoring the best upcoming graphics researcher in Europe. In addition to his academic impact, Prof. Nießner is a co-founder and director of Synthesia Inc., a brand-new startup backed by Marc Cuban, whose aim is to empower storytellers with cutting-edge AI-driven video synthesis.
Prof. Heng Tao Shen
University of Electronic Science and Technology of China
Talk Title:  Cross-Media Intelligence
Abstract: It has been shown that heterogeneous multimedia data gathered from different sources in different media types can be often correlated and linked to the same knowledge space. Towards cross-media intelligence, cross-media understanding, retrieval and interaction has attracted huge amount of attention due to its significance in both research communities and industries. In this talk, we will introduce the state of the art on this topic and discuss its future trends.
Biography: Professor Heng Tao Shen, ACM Fellow and OSA Fellow, is Dean of School of Computer Science and Engineering and Executive Dean of AI Research Institute at University of Electronic Science and Technology of China (UESTC). He obtained his BSc with First Class Honours and PhD from Department of Computer Science at National University of Singapore in 2000 and 2004 respectively. He was a professor at the University of Queensland before joining UESTC. His research has made contributions to the field of hashing big multimedia data, from hashing theory, to algorithms and applications, and he has led the charge to address the challenging problem of cross-media understanding and retrieval. He has published 300+ peer-reviewed papers, including 110+ IEEE/ACM Transactions, and 200+ CCF-A ranked papers. He has received 8 Best Paper Awards, including  ACM Multimedia 2017, ACM SIGIR 2017, and IEEE Transactions on Multimedia 2020. He is General Co-Chair of ACM Multimedia 2021, former TPC Co-Chair of ACM Multimedia 2015, and an Associate Editor of ACM Transactions of Data Science, IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Knowledge and Data Engineering, Pattern Recognition, and Journal of Software.


Conference Website



http://icig2021.csig.org.cn/

To visit  the conference website, please scan the following QR code:



Online Payment



http://conf.csig.org.cn/fair/394

To register on the microsite, please scan the following QR code:




中国图象图形学学会关于开展第七届中国科协青年人才托举工程项目推荐工作的通知
中国图象图形学学会关于组织开展科技成果鉴定的通知

CSIG图像图形中国行承办方征集中

登录查看更多
0

相关内容

命名实体识别(NER)(也称为实体标识,实体组块和实体提取)是信息抽取的子任务,旨在将非结构化文本中提到的命名实体定位和分类为预定义类别,例如人员姓名、地名、机构名、专有名词等。

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等
Artificial Intelligence: Ready to Ride the Wave? BCG 28页PPT
专知会员服务
26+阅读 · 2022年2月20日
专知会员服务
38+阅读 · 2020年9月6日
Linux导论,Introduction to Linux,96页ppt
专知会员服务
75+阅读 · 2020年7月26日
强化学习最新教程,17页pdf
专知会员服务
166+阅读 · 2019年10月11日
机器学习入门的经验与建议
专知会员服务
89+阅读 · 2019年10月10日
【哈佛大学商学院课程Fall 2019】机器学习可解释性
专知会员服务
96+阅读 · 2019年10月9日
【SIGGRAPH2019】TensorFlow 2.0深度学习计算机图形学应用
专知会员服务
39+阅读 · 2019年10月9日
【ICIG2021】Latest News & Announcements of the Tutorial
中国图象图形学学会CSIG
2+阅读 · 2021年12月20日
【ICIG2021】Latest News & Announcements of the Workshop
中国图象图形学学会CSIG
0+阅读 · 2021年12月20日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium8
中国图象图形学学会CSIG
0+阅读 · 2021年11月16日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium5
中国图象图形学学会CSIG
1+阅读 · 2021年11月11日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium3
中国图象图形学学会CSIG
0+阅读 · 2021年11月9日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium2
中国图象图形学学会CSIG
0+阅读 · 2021年11月8日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium1
中国图象图形学学会CSIG
0+阅读 · 2021年11月3日
【ICIG2021】Latest News & Announcements of the Plenary Talk2
中国图象图形学学会CSIG
0+阅读 · 2021年11月2日
【ICIG2021】Latest News & Announcements of the Industry Talk2
中国图象图形学学会CSIG
0+阅读 · 2021年7月29日
【ICIG2021】Latest News & Announcements of the Industry Talk1
中国图象图形学学会CSIG
0+阅读 · 2021年7月28日
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
0+阅读 · 2013年12月31日
国家自然科学基金
1+阅读 · 2012年12月31日
国家自然科学基金
0+阅读 · 2011年12月31日
Arxiv
0+阅读 · 2022年4月20日
Arxiv
0+阅读 · 2022年4月20日
Arxiv
29+阅读 · 2022年2月15日
Arxiv
14+阅读 · 2020年2月6日
VIP会员
相关VIP内容
Artificial Intelligence: Ready to Ride the Wave? BCG 28页PPT
专知会员服务
26+阅读 · 2022年2月20日
专知会员服务
38+阅读 · 2020年9月6日
Linux导论,Introduction to Linux,96页ppt
专知会员服务
75+阅读 · 2020年7月26日
强化学习最新教程,17页pdf
专知会员服务
166+阅读 · 2019年10月11日
机器学习入门的经验与建议
专知会员服务
89+阅读 · 2019年10月10日
【哈佛大学商学院课程Fall 2019】机器学习可解释性
专知会员服务
96+阅读 · 2019年10月9日
【SIGGRAPH2019】TensorFlow 2.0深度学习计算机图形学应用
专知会员服务
39+阅读 · 2019年10月9日
相关资讯
【ICIG2021】Latest News & Announcements of the Tutorial
中国图象图形学学会CSIG
2+阅读 · 2021年12月20日
【ICIG2021】Latest News & Announcements of the Workshop
中国图象图形学学会CSIG
0+阅读 · 2021年12月20日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium8
中国图象图形学学会CSIG
0+阅读 · 2021年11月16日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium5
中国图象图形学学会CSIG
1+阅读 · 2021年11月11日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium3
中国图象图形学学会CSIG
0+阅读 · 2021年11月9日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium2
中国图象图形学学会CSIG
0+阅读 · 2021年11月8日
【ICIG2021】Check out the hot new trailer of ICIG2021 Symposium1
中国图象图形学学会CSIG
0+阅读 · 2021年11月3日
【ICIG2021】Latest News & Announcements of the Plenary Talk2
中国图象图形学学会CSIG
0+阅读 · 2021年11月2日
【ICIG2021】Latest News & Announcements of the Industry Talk2
中国图象图形学学会CSIG
0+阅读 · 2021年7月29日
【ICIG2021】Latest News & Announcements of the Industry Talk1
中国图象图形学学会CSIG
0+阅读 · 2021年7月28日
相关基金
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
0+阅读 · 2014年12月31日
国家自然科学基金
0+阅读 · 2013年12月31日
国家自然科学基金
1+阅读 · 2012年12月31日
国家自然科学基金
0+阅读 · 2011年12月31日
Top
微信扫码咨询专知VIP会员